Package 'SLmetrics' reference manual

Title:	Machine Learning Performance Evaluation on Steroids
Description:	Performance evaluation metrics for supervised and unsupervised machine learning, statistical learning and artificial intelligence applications. Core computations are implemented in 'C++' for scalability and efficiency.
Authors:	Serkan Korkmaz [cre, aut, cph] (ORCID: <https://orcid.org/0000-0002-5052-0982>)
Maintainer:	Serkan Korkmaz <[email protected]>
License:	GPL (>= 3)
Version:	0.3-4
Built:	2026-06-04 06:59:17 UTC
Source:	https://github.com/serkor1/SLmetrics

Accuracy

Description

A generic S3 function to compute the accuracy score for a classification model. This function dispatches to S3 methods in accuracy() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because accuracy() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap accuracy() in a "safe" validator that checks for NA values and matching length, for example:

safe_accuracy <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  accuracy(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

For multiple performance evaluations of a classification model, first compute the confusion matrix once via cmatrix(). All other performance metrics can then be derived from this one object via S3 dispatching:

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate accuracy
## via S3 dispatching
accuracy(confusion_matrix)

## additional performance metrics
## below

The accuracy.factor() method calls cmatrix() internally, so explicitly invoking accuracy.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## Generic S3 method
## for Accuracy
accuracy(...)

## Generic S3 method
## for weighted Accuracy
weighted.accuracy(...)
## Generic S3 method
## for Accuracy
accuracy(...)

## Generic S3 method
## for weighted Accuracy
weighted.accuracy(...)

Arguments

...

Arguments passed on to accuracy.factor, weighted.accuracy.factor, accuracy.cmatrix

actual,predicted: A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.
w: A <double> vector of sample weights.
x: A confusion matrix created cmatrix().

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::accuracy(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::accuracy(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Accuracy

Description

Defensive measures

To guard against this, wrap accuracy() in a "safe" validator that checks for NA values and matching length, for example:

safe_accuracy <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  accuracy(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate accuracy
## via S3 dispatching
accuracy(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
accuracy(x, ...)
## S3 method for class 'cmatrix'
accuracy(x, ...)

Arguments

x

A confusion matrix created cmatrix().

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::accuracy(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::accuracy(confusion_matrix)

Accuracy

Description

Defensive measures

To guard against this, wrap accuracy() in a "safe" validator that checks for NA values and matching length, for example:

safe_accuracy <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  accuracy(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate accuracy
## via S3 dispatching
accuracy(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
accuracy(actual, predicted, ...)
## S3 method for class 'factor'
accuracy(actual, predicted, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::accuracy(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::accuracy(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Area under the Precision Recall Curve

Description

A generic S3 function to compute the area under the precision recall curve score for a classification model. This function dispatches to S3 methods in auc.pr.curve() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because auc.pr.curve() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap auc.pr.curve() in a "safe" validator that checks for NA values and matching length, for example:

safe_auc.pr.curve <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  auc.pr.curve(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Visualizing area under the precision recall curve

Use pr.curve() to construct the data.frame and use plot to visualize the area under the curve.

Efficient multi-metric evaluation

To avoid sorting the same probability matrix multiple times (once per class or curve), you can precompute a single set of sort indices and pass it via the indices argument. This reduces the overall cost from O(K·N log N) to O(N log N + K·N).

## presort response
## probabilities
indices <- preorder(response, decreasing = TRUE)

## evaluate area under the precision recall curve
auc.pr.curve(actual, response, indices = indices)

Usage

## Generic S3 method
## for Area under the Precision Recall Curve
auc.pr.curve(...)

## Generic S3 method for
## unweighted area under the
## Precision Recall Curve
auc.pr.curve(...)

## Generic S3 method
## for weighted Area under the Precision Recall Curve
weighted.auc.pr.curve(...)
## Generic S3 method
## for Area under the Precision Recall Curve
auc.pr.curve(...)

## Generic S3 method for
## unweighted area under the
## Precision Recall Curve
auc.pr.curve(...)

## Generic S3 method
## for weighted Area under the Precision Recall Curve
weighted.auc.pr.curve(...)

Arguments

...

Arguments passed on to auc.pr.curve.factor, weighted.auc.pr.curve.factor

actual

A vector length $n$ , and $k$ levels. Can be of integer or factor.

response

A $n \times k$ <double>-matrix of predicted probabilities. The $i$ -th row should sum to 1 (i.e., a valid probability distribution over the $k$ classes). The first column corresponds to the first factor level in actual, the second column to the second factor level, and so on.

method

A <double> value (default: $0$ ). Defines the underlying method of calculating the area under the curve. If $0$ it is calculated using the trapezoid-method, if $1$ it is calculated using the step-method.

indices

An optional $n \times k$ matrix of <integer> values of sorted response probability indices.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

w

A <double> vector of sample weights.

Value

If estimator is given as

0: a named <double>-vector of length k
1: a <double> value (Micro averaged metric)
2: a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)

## Calculate area under the precision recall curve

SLmetrics::auc.pr.curve(
    actual   = actual_classes, 
    response = probability_matrix
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)

## Calculate area under the precision recall curve

SLmetrics::auc.pr.curve(
    actual   = actual_classes, 
    response = probability_matrix
)

Area under the Precision Recall Curve

Description

Defensive measures

To guard against this, wrap auc.pr.curve() in a "safe" validator that checks for NA values and matching length, for example:

safe_auc.pr.curve <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  auc.pr.curve(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Visualizing area under the precision recall curve

Use pr.curve() to construct the data.frame and use plot to visualize the area under the curve.

Efficient multi-metric evaluation

## presort response
## probabilities
indices <- preorder(response, decreasing = TRUE)

## evaluate area under the precision recall curve
auc.pr.curve(actual, response, indices = indices)

Usage

## S3 method for class 'factor'
auc.pr.curve(
  actual,
  response,
  estimator = 0L,
  method = 0L,
  indices = NULL,
  ...
)
## S3 method for class 'factor'
auc.pr.curve(
  actual,
  response,
  estimator = 0L,
  method = 0L,
  indices = NULL,
  ...
)

Arguments

actual

A vector length $n$ , and $k$ levels. Can be of integer or factor.

response

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

method

indices

An optional $n \times k$ matrix of <integer> values of sorted response probability indices.

...

Arguments passed into other methods.

Value

If estimator is given as

0: a named <double>-vector of length k
1: a <double> value (Micro averaged metric)
2: a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)


## Evaluate performance

SLmetrics::auc.pr.curve(
    actual   = actual_classes, 
    response = probability_matrix
)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)


## Evaluate performance

SLmetrics::auc.pr.curve(
    actual   = actual_classes, 
    response = probability_matrix
)

Area under the Receiver Operator Characteristics Curve

Description

A generic S3 function to compute the area under the receiver operator characteristics curve score for a classification model. This function dispatches to S3 methods in auc.roc.curve() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because auc.roc.curve() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap auc.roc.curve() in a "safe" validator that checks for NA values and matching length, for example:

safe_auc.roc.curve <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  auc.roc.curve(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Visualizing area under the receiver operator characteristics curve

Use roc.curve() to construct the data.frame and use plot to visualize the area under the curve.

Efficient multi-metric evaluation

## presort response
## probabilities
indices <- preorder(response, decreasing = TRUE)

## evaluate area under the receiver operator characteristics curve
auc.roc.curve(actual, response, indices = indices)

Usage

## Generic S3 method
## for Area under the Receiver Operator Characteristics Curve
auc.roc.curve(...)

## Generic S3 method for
## unweighted area under the
## Receiver Operator Characteristics
## Curve
auc.roc.curve(...)

## Generic S3 method
## for weighted Area under the Receiver Operator Characteristics Curve
weighted.auc.roc.curve(...)
## Generic S3 method
## for Area under the Receiver Operator Characteristics Curve
auc.roc.curve(...)

## Generic S3 method for
## unweighted area under the
## Receiver Operator Characteristics
## Curve
auc.roc.curve(...)

## Generic S3 method
## for weighted Area under the Receiver Operator Characteristics Curve
weighted.auc.roc.curve(...)

Arguments

...

Arguments passed on to auc.roc.curve.factor, weighted.auc.roc.curve.factor

actual

A vector length $n$ , and $k$ levels. Can be of integer or factor.

response

method

indices

An optional $n \times k$ matrix of <integer> values of sorted response probability indices.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

w

A <double> vector of sample weights.

Value

If estimator is given as

0: a named <double>-vector of length k
1: a <double> value (Micro averaged metric)
2: a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)

## Calculate area under the receiver operator characteristics curve

SLmetrics::auc.roc.curve(
    actual   = actual_classes, 
    response = probability_matrix
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)

## Calculate area under the receiver operator characteristics curve

SLmetrics::auc.roc.curve(
    actual   = actual_classes, 
    response = probability_matrix
)

Area under the Receiver Operator Characteristics Curve

Description

Defensive measures

To guard against this, wrap auc.roc.curve() in a "safe" validator that checks for NA values and matching length, for example:

safe_auc.roc.curve <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  auc.roc.curve(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Visualizing area under the receiver operator characteristics curve

Use roc.curve() to construct the data.frame and use plot to visualize the area under the curve.

Efficient multi-metric evaluation

## presort response
## probabilities
indices <- preorder(response, decreasing = TRUE)

## evaluate area under the receiver operator characteristics curve
auc.roc.curve(actual, response, indices = indices)

Usage

## S3 method for class 'factor'
auc.roc.curve(
  actual,
  response,
  estimator = 0L,
  method = 0L,
  indices = NULL,
  ...
)
## S3 method for class 'factor'
auc.roc.curve(
  actual,
  response,
  estimator = 0L,
  method = 0L,
  indices = NULL,
  ...
)

Arguments

actual

A vector length $n$ , and $k$ levels. Can be of integer or factor.

response

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

method

indices

An optional $n \times k$ matrix of <integer> values of sorted response probability indices.

...

Arguments passed into other methods.

Value

If estimator is given as

0: a named <double>-vector of length k
1: a <double> value (Micro averaged metric)
2: a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)


## Evaluate performance

SLmetrics::auc.roc.curve(
    actual   = actual_classes, 
    response = probability_matrix
)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)


## Evaluate performance

SLmetrics::auc.roc.curve(
    actual   = actual_classes, 
    response = probability_matrix
)

Area under the curve

Description

The auc.xy()-function calculates the area under the curve.

Usage

## Generic S3 method
auc.xy(...)
## Generic S3 method
auc.xy(...)

Arguments

...

Arguments passed on to auc.xy.numeric

y,x: A pair of <double> vectors of length $n$ .
method: A <integer> value (default: $0$ ). Defines the underlying method of calculating the area under the curve. If $0$ it is calculated using the trapezoid-method, if $1$ it is calculated using the step-method.
presorted: A <logical>-value length 1 (default: FALSE). If TRUE the input will not be sorted by threshold.

Value

A <double> value.

Definition

Trapezoidal rule

The trapezoidal rule approximates the integral of a function $f(x)$ between $x = a$ and $x = b$ using trapezoids formed between consecutive points. If we have points $x_0, x_1, \ldots, x_n$ (with $a = x_0 < x_1 < \cdots < x_n = b$ ) and corresponding function values $f(x_0), f(x_1), \ldots, f(x_n)$ , the area under the curve $A_T$ is approximated by:

$A_T \approx \sum_{k=1}^{n} \frac{f(x_{k-1}) + f(x_k)}{2} \bigl[x_k - x_{k-1}\bigr].$

Step-function method

The step-function (rectangular) method uses the value of the function at one endpoint of each subinterval to form rectangles. With the same partition $x_0, x_1, \ldots, x_n$ , the rectangular approximation $A_S$ can be written as:

$A_S \approx \sum_{k=1}^{n} f(x_{k-1}) \bigl[x_k - x_{k-1}\bigr].$

Area under the curve

Description

Area under the curve

Usage

## S3 method for class 'numeric'
auc.xy(y, x, method = 0L, presorted = TRUE, ...)
## S3 method for class 'numeric'
auc.xy(y, x, method = 0L, presorted = TRUE, ...)

Arguments

y, x

A pair of <double> vectors of length $n$ .

method

A <integer> value (default: $0$ ). Defines the underlying method of calculating the area under the curve. If $0$ it is calculated using the trapezoid-method, if $1$ it is calculated using the step-method.

presorted

A <logical>-value length 1 (default: FALSE). If TRUE the input will not be sorted by threshold.

...

Arguments passed into other methods.

Balanced Accuracy

Description

A generic S3 function to compute the balanced accuracy score for a classification model. This function dispatches to S3 methods in baccuracy() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because baccuracy() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap baccuracy() in a "safe" validator that checks for NA values and matching length, for example:

safe_baccuracy <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  baccuracy(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate balanced accuracy
## via S3 dispatching
baccuracy(confusion_matrix)

## additional performance metrics
## below

The baccuracy.factor() method calls cmatrix() internally, so explicitly invoking baccuracy.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## Generic S3 method
## for Balanced Accuracy
baccuracy(...)

## Generic S3 method
## for weighted Balanced Accuracy
weighted.baccuracy(...)
## Generic S3 method
## for Balanced Accuracy
baccuracy(...)

## Generic S3 method
## for weighted Balanced Accuracy
weighted.baccuracy(...)

Arguments

...

Arguments passed on to baccuracy.factor, weighted.baccuracy.factor, baccuracy.cmatrix

adjust: A <logical> value (default: FALSE). If TRUE the metric is adjusted for random chance $\frac{1}{k}$ .
actual,predicted: A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.
na.rm: A <logical> value of length $1$ (default: TRUE). If TRUE, NA values are removed from the computation. This argument is only relevant when micro != NULL. When na.rm = TRUE, the computation corresponds to sum(c(1, 2, NA), na.rm = TRUE) / length(na.omit(c(1, 2, NA))). When na.rm = FALSE, the computation corresponds to sum(c(1, 2, NA), na.rm = TRUE) / length(c(1, 2, NA)).
w: A <double> vector of sample weights.
x: A confusion matrix created cmatrix().

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::baccuracy(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::baccuracy(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Balanced Accuracy

Description

Defensive measures

To guard against this, wrap baccuracy() in a "safe" validator that checks for NA values and matching length, for example:

safe_baccuracy <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  baccuracy(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate balanced accuracy
## via S3 dispatching
baccuracy(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
baccuracy(x, adjust = FALSE, na.rm = TRUE, ...)
## S3 method for class 'cmatrix'
baccuracy(x, adjust = FALSE, na.rm = TRUE, ...)

Arguments

x

A confusion matrix created cmatrix().

adjust

A <logical> value (default: FALSE). If TRUE the metric is adjusted for random chance $\frac{1}{k}$ .

na.rm

A <logical> value of length $1$ (default: TRUE). If TRUE, NA values are removed from the computation. This argument is only relevant when micro != NULL. When na.rm = TRUE, the computation corresponds to sum(c(1, 2, NA), na.rm = TRUE) / length(na.omit(c(1, 2, NA))). When na.rm = FALSE, the computation corresponds to sum(c(1, 2, NA), na.rm = TRUE) / length(c(1, 2, NA)).

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::baccuracy(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::baccuracy(confusion_matrix)

Balanced Accuracy

Description

Defensive measures

To guard against this, wrap baccuracy() in a "safe" validator that checks for NA values and matching length, for example:

safe_baccuracy <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  baccuracy(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate balanced accuracy
## via S3 dispatching
baccuracy(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
baccuracy(actual, predicted, adjust = FALSE, na.rm = TRUE, ...)
## S3 method for class 'factor'
baccuracy(actual, predicted, adjust = FALSE, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

adjust

A <logical> value (default: FALSE). If TRUE the metric is adjusted for random chance $\frac{1}{k}$ .

na.rm

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::baccuracy(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::baccuracy(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Banknote authentication dataset

Description

This dataset contains features extracted from the wavelet transform of banknote images, which are used to classify banknotes as authentic or inauthentic. The data originates from the UCI Machine Learning Repository.

The data is provided as a list with two components:

features

A data frame containing the following variables:

variance: Variance of the wavelet transformed image.
skewness: Skewness of the wavelet transformed image.
curtosis: Curtosis of the wavelet transformed image.
entropy: Entropy of the image.

target

A factor indicating the authenticity of the banknote. The factor has two levels:

inauthentic: Indicates the banknote is not genuine.
authentic: Indicates the banknote is genuine.

Usage

data(banknote)
data(banknote)

Format

A list with two components:

features: A data frame with 4 variables: variance, skewness, curtosis, and entropy.
target: A factor with levels "inauthentic" and "authentic" representing the banknote's authenticity.

References

Gillich, Eugen & Lohweg, Volker. (2010). Banknote Authentication.

Brier Score

Description

A generic S3 function to compute the brier score score for a classification model. This function dispatches to S3 methods in brier.score() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because brier.score() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap brier.score() in a "safe" validator that checks for NA values and matching length, for example:

safe_brier.score <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  brier.score(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Brier Score
brier.score(...)

## Generic S3 method
## for weighted Brier Score
weighted.brier.score(...)
## Generic S3 method
## for Brier Score
brier.score(...)

## Generic S3 method
## for weighted Brier Score
weighted.brier.score(...)

Arguments

...

Arguments passed on to brier.score.matrix, weighted.brier.score.matrix

ok: A <double> indicator matrix with $n$ samples and $k$ classes.
qk: A $n \times k$ <double>-matrix of predicted probabilities. The $i$ -th row should sum to 1 (i.e., a valid probability distribution over the $k$ classes). The first column corresponds to the first factor level in actual, the second column to the second factor level, and so on.
w: A <double> vector of sample weights.

Value

A <double>-value

References

Gneiting, Tilmann, and Adrian E. Raftery. "Strictly proper scoring rules, prediction, and estimation." Journal of the American statistical Association 102.477 (2007): 359-378.

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## seed
set.seed(1903)

## The general setup
## with 3 classes
n_obs     <- 10
n_classes <- 3

## Generate indicator matrix
## with observed outcome (ok) and 
## its predicted probability matrix (qk)
ok <- diag(n_classes)[ sample.int(n_classes, n_obs, TRUE), ]
qk <- matrix(runif(n_obs * n_classes), n_obs, n_classes)
qk <- qk / rowSums(qk)

## Evaluate performance
SLmetrics::brier.score(
   ok = ok, 
   qk = qk
)

## seed
set.seed(1903)

## The general setup
## with 3 classes
n_obs     <- 10
n_classes <- 3

## Generate indicator matrix
## with observed outcome (ok) and 
## its predicted probability matrix (qk)
ok <- diag(n_classes)[ sample.int(n_classes, n_obs, TRUE), ]
qk <- matrix(runif(n_obs * n_classes), n_obs, n_classes)
qk <- qk / rowSums(qk)

## Evaluate performance
SLmetrics::brier.score(
   ok = ok, 
   qk = qk
)

Brier Score

Description

Defensive measures

To guard against this, wrap brier.score() in a "safe" validator that checks for NA values and matching length, for example:

safe_brier.score <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  brier.score(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'matrix'
brier.score(ok, qk, ...)
## S3 method for class 'matrix'
brier.score(ok, qk, ...)

Arguments

ok

A <double> indicator matrix with $n$ samples and $k$ classes.

qk

...

Arguments passed into other methods.

Value

A <double>-value

References

Gneiting, Tilmann, and Adrian E. Raftery. "Strictly proper scoring rules, prediction, and estimation." Journal of the American statistical Association 102.477 (2007): 359-378.

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## seed
set.seed(1903)

## The general setup
## with 3 classes
n_obs     <- 10
n_classes <- 3

## Generate indicator matrix
## with observed outcome (ok) and 
## its predicted probability matrix (qk)
ok <- diag(n_classes)[ sample.int(n_classes, n_obs, TRUE), ]
qk <- matrix(runif(n_obs * n_classes), n_obs, n_classes)
qk <- qk / rowSums(qk)

## Evaluate performance
SLmetrics::brier.score(
   ok = ok, 
   qk = qk
)


## seed
set.seed(1903)

## The general setup
## with 3 classes
n_obs     <- 10
n_classes <- 3

## Generate indicator matrix
## with observed outcome (ok) and 
## its predicted probability matrix (qk)
ok <- diag(n_classes)[ sample.int(n_classes, n_obs, TRUE), ]
qk <- matrix(runif(n_obs * n_classes), n_obs, n_classes)
qk <- qk / rowSums(qk)

## Evaluate performance
SLmetrics::brier.score(
   ok = ok, 
   qk = qk
)

Concordance Correlation Coefficient

Description

A generic S3 function to compute the concordance correlation coefficient score for a regression model. This function dispatches to S3 methods in ccc() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because ccc() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap ccc() in a "safe" validator that checks for NA values and matching length, for example:

safe_ccc <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  ccc(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Concordance Correlation Coefficient
ccc(...)

## Generic S3 method
## for weighted Concordance Correlation Coefficient
weighted.ccc(...)
## Generic S3 method
## for Concordance Correlation Coefficient
ccc(...)

## Generic S3 method
## for weighted Concordance Correlation Coefficient
weighted.ccc(...)

Arguments

...

Arguments passed on to ccc.numeric, weighted.ccc.numeric

actual,predicted: A pair of <double> vectors of length $n$ .
correction: A <logical> vector of length $1$ (default: FALSE). If TRUE the variance and covariance will be adjusted with $\frac{1-n}{n}$
w: A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::ccc(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::ccc(
   actual    = actual_values, 
   predicted = predicted_values
)

Concordance Correlation Coefficient

Description

Defensive measures

To guard against this, wrap ccc() in a "safe" validator that checks for NA values and matching length, for example:

safe_ccc <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  ccc(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
ccc(actual, predicted, correction = FALSE, ...)
## S3 method for class 'numeric'
ccc(actual, predicted, correction = FALSE, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

correction

A <logical> vector of length $1$ (default: FALSE). If TRUE the variance and covariance will be adjusted with $\frac{1-n}{n}$

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::ccc(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::ccc(
   actual    = actual_values, 
   predicted = predicted_values
)

Cohen's $\kappa$ -Statistic

Description

A generic S3 function to compute the cohen's $\kappa$ -statistic score for a classification model. This function dispatches to S3 methods in ckappa() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because ckappa() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap ckappa() in a "safe" validator that checks for NA values and matching length, for example:

safe_ckappa <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  ckappa(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate cohen's \eqn{\kappa}-statistic
## via S3 dispatching
ckappa(confusion_matrix)

## additional performance metrics
## below

The ckappa.factor() method calls cmatrix() internally, so explicitly invoking ckappa.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## Generic S3 method
## for Cohen's \eqn{\kappa}-Statistic
ckappa(...)

## Generic S3 method
## for weighted Cohen's \eqn{\kappa}-Statistic
weighted.ckappa(...)
## Generic S3 method
## for Cohen's \eqn{\kappa}-Statistic
ckappa(...)

## Generic S3 method
## for weighted Cohen's \eqn{\kappa}-Statistic
weighted.ckappa(...)

Arguments

...

Arguments passed on to ckappa.factor, weighted.ckappa.factor, ckappa.cmatrix

beta: A <double> value of length 1 (default: 0). If $\beta \neq 0$ the off-diagonals of the confusion matrix are penalized with a factor of $(y_{+} - y_{i,-})^\beta$ .
actual,predicted: A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.
w: A <double> vector of sample weights.
x: A confusion matrix created cmatrix().

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::ckappa(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::ckappa(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Cohen's $\kappa$ -Statistic

Description

Defensive measures

To guard against this, wrap ckappa() in a "safe" validator that checks for NA values and matching length, for example:

safe_ckappa <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  ckappa(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate cohen's \eqn{\kappa}-statistic
## via S3 dispatching
ckappa(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
ckappa(x, beta = 0, ...)
## S3 method for class 'cmatrix'
ckappa(x, beta = 0, ...)

Arguments

x

A confusion matrix created cmatrix().

beta

A <double> value of length 1 (default: 0). If $\beta \neq 0$ the off-diagonals of the confusion matrix are penalized with a factor of $(y_{+} - y_{i,-})^\beta$ .

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::ckappa(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::ckappa(confusion_matrix)

Cohen's $\kappa$ -Statistic

Description

Defensive measures

To guard against this, wrap ckappa() in a "safe" validator that checks for NA values and matching length, for example:

safe_ckappa <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  ckappa(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate cohen's \eqn{\kappa}-statistic
## via S3 dispatching
ckappa(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
ckappa(actual, predicted, beta = 0, ...)
## S3 method for class 'factor'
ckappa(actual, predicted, beta = 0, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

beta

A <double> value of length 1 (default: 0). If $\beta \neq 0$ the off-diagonals of the confusion matrix are penalized with a factor of $(y_{+} - y_{i,-})^\beta$ .

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::ckappa(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::ckappa(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Confusion Matrix

Description

A generic S3 function to compute the confusion matrix for a classification model. This function dispatches to S3 methods in cmatrix() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because cmatrix() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap cmatrix() in a "safe" validator that checks for NA values and matching length, for example:

safe_cmatrix <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  cmatrix(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

The workhorse

cmatrix() is the main function for classification metrics with cmatrix S3 dispatch. These functions internally calls cmatrix(), so there is a signficant gain in computing the confusion matrix first, and then pass it onto the metrics. For example:

## Compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## Evaluate accuracy
## via S3 dispatching
accuracy(confusion_matrix)

## Evaluate recall
## via S3 dispatching
recall(confusion_matrix)

Usage

## Generic S3 method
## for Confusion Matrix
cmatrix(...)

## Generic S3 method
## for weighted Confusion Matrix
weighted.cmatrix(...)
## Generic S3 method
## for Confusion Matrix
cmatrix(...)

## Generic S3 method
## for weighted Confusion Matrix
weighted.cmatrix(...)

Arguments

...

Arguments passed on to cmatrix.factor, weighted.cmatrix.factor

actual,predicted: A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.
w: A <double> vector of sample weights.

Value

A named $k$ x $k$ <matrix>

Dimensions

There is no robust defensive measure against misspecifying the confusion matrix. If the arguments are passed correctly, the resulting confusion matrix is on the form:

	A (Predicted)	B (Predicted)
A (Actual)	Value	Value
B (Actual)	Value	Value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Compute the confusion
## matrix
SLmetrics::cmatrix(
 actual    = actual_classes, 
 predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Compute the confusion
## matrix
SLmetrics::cmatrix(
 actual    = actual_classes, 
 predicted = predicted_classes
)

Confusion Matrix

Description

Defensive measures

To guard against this, wrap cmatrix() in a "safe" validator that checks for NA values and matching length, for example:

safe_cmatrix <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  cmatrix(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

The workhorse

## Compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## Evaluate accuracy
## via S3 dispatching
accuracy(confusion_matrix)

## Evaluate recall
## via S3 dispatching
recall(confusion_matrix)

Usage

## S3 method for class 'factor'
cmatrix(actual, predicted, ...)
## S3 method for class 'factor'
cmatrix(actual, predicted, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

...

Arguments passed into other methods.

Value

A named $k$ x $k$ <matrix>

Dimensions

There is no robust defensive measure against misspecifying the confusion matrix. If the arguments are passed correctly, the resulting confusion matrix is on the form:

	A (Predicted)	B (Predicted)
A (Actual)	Value	Value
B (Actual)	Value	Value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Compute confusion matrix
SLmetrics::cmatrix(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Compute confusion matrix
SLmetrics::cmatrix(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Cross Entropy

Description

A generic S3 function to compute the cross entropy score for a classification model. This function dispatches to S3 methods in cross.entropy() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because cross.entropy() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap cross.entropy() in a "safe" validator that checks for NA values and matching length, for example:

safe_cross.entropy <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  cross.entropy(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Cross Entropy
cross.entropy(...)
## Generic S3 method
## for Cross Entropy
cross.entropy(...)

Arguments

...

Arguments passed on to cross.entropy.matrix

pk,qk: A pair of <double> matrices of length $n$ of emprical probabilities $p$ and estimated probabilities $q$ .
dim: An <integer> value of length 1 (Default: 0). Defines the dimension along which to calculate the entropy (0: total, 1: row-wise, 2: column-wise).
normalize: A <logical>-value (default: TRUE). If TRUE, the mean cross-entropy across all observations is returned; otherwise, the sum of cross-entropies is returned.

Value

A <double> value or vector:

A single <double> value (length 1) if dim == 0.
A <double> vector with length equal to the length of columns if dim == 1.
A <double> vector with length equal to the length of rows if dim == 2.

References

MacKay, David JC. Information theory, inference and learning algorithms. Cambridge university press, 2003.

Kramer, Oliver, and Oliver Kramer. "Scikit-learn." Machine learning for evolution strategies (2016): 45-53.

Virtanen, Pauli, et al. "SciPy 1.0: f'undamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## generate valid probability
## distributions
rand.sum <- function(n) {
   x <- sort(runif( n-1 ))
   c(x,1) - c(0, x)
}

## empirical and
## predicted probabilites
set.seed(1903)
pk <- t(replicate(200,rand.sum(5)))
qk <- t(replicate(200,rand.sum(5)))

## entropy
cross.entropy(
 pk = pk,
 qk = qk
)



## generate valid probability
## distributions
rand.sum <- function(n) {
   x <- sort(runif( n-1 ))
   c(x,1) - c(0, x)
}

## empirical and
## predicted probabilites
set.seed(1903)
pk <- t(replicate(200,rand.sum(5)))
qk <- t(replicate(200,rand.sum(5)))

## entropy
cross.entropy(
 pk = pk,
 qk = qk
)

Cross Entropy

Description

Defensive measures

To guard against this, wrap cross.entropy() in a "safe" validator that checks for NA values and matching length, for example:

safe_cross.entropy <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  cross.entropy(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'matrix'
cross.entropy(pk, qk, dim = 0L, normalize = FALSE, ...)
## S3 method for class 'matrix'
cross.entropy(pk, qk, dim = 0L, normalize = FALSE, ...)

Arguments

pk, qk

A pair of <double> matrices of length $n$ of emprical probabilities $p$ and estimated probabilities $q$ .

dim

An <integer> value of length 1 (Default: 0). Defines the dimension along which to calculate the entropy (0: total, 1: row-wise, 2: column-wise).

normalize

A <logical>-value (default: TRUE). If TRUE, the mean cross-entropy across all observations is returned; otherwise, the sum of cross-entropies is returned.

...

Arguments passed into other methods.

Value

A <double> value or vector:

A single <double> value (length 1) if dim == 0.
A <double> vector with length equal to the length of columns if dim == 1.
A <double> vector with length equal to the length of rows if dim == 2.

References

MacKay, David JC. Information theory, inference and learning algorithms. Cambridge university press, 2003.

Kramer, Oliver, and Oliver Kramer. "Scikit-learn." Machine learning for evolution strategies (2016): 45-53.

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## generate valid probability
## distributions
rand.sum <- function(n) {
   x <- sort(runif( n-1 ))
   c(x,1) - c(0, x)
}

## empirical and
## predicted probabilites
set.seed(1903)
pk <- t(replicate(200,rand.sum(5)))
qk <- t(replicate(200,rand.sum(5)))

## entropy
cross.entropy(
 pk = pk,
 qk = qk
)



## generate valid probability
## distributions
rand.sum <- function(n) {
   x <- sort(runif( n-1 ))
   c(x,1) - c(0, x)
}

## empirical and
## predicted probabilites
set.seed(1903)
pk <- t(replicate(200,rand.sum(5)))
qk <- t(replicate(200,rand.sum(5)))

## entropy
cross.entropy(
 pk = pk,
 qk = qk
)

Gamma Deviance

Description

A generic S3 function to compute the gamma deviance score for a regression model. This function dispatches to S3 methods in deviance.gamma() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because deviance.gamma() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap deviance.gamma() in a "safe" validator that checks for NA values and matching length, for example:

safe_deviance.gamma <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  deviance.gamma(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Gamma Deviance
deviance.gamma(...)

## Generic S3 method
## for weighted Gamma Deviance
weighted.deviance.gamma(...)
## Generic S3 method
## for Gamma Deviance
deviance.gamma(...)

## Generic S3 method
## for weighted Gamma Deviance
weighted.deviance.gamma(...)

Arguments

...

Arguments passed on to deviance.gamma.numeric, weighted.deviance.gamma.numeric

actual,predicted: A pair of <double> vectors of length $n$ .
w: A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::deviance.gamma(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::deviance.gamma(
   actual    = actual_values, 
   predicted = predicted_values
)

Gamma Deviance

Description

Defensive measures

To guard against this, wrap deviance.gamma() in a "safe" validator that checks for NA values and matching length, for example:

safe_deviance.gamma <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  deviance.gamma(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'gamma.numeric'
deviance(actual, predicted, ...)
## S3 method for class 'gamma.numeric'
deviance(actual, predicted, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::deviance.gamma(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::deviance.gamma(
   actual    = actual_values, 
   predicted = predicted_values
)

Poisson Deviance

Description

A generic S3 function to compute the poisson deviance score for a regression model. This function dispatches to S3 methods in deviance.poisson() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because deviance.poisson() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap deviance.poisson() in a "safe" validator that checks for NA values and matching length, for example:

safe_deviance.poisson <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  deviance.poisson(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Poisson Deviance
deviance.poisson(...)

## Generic S3 method
## for weighted Poisson Deviance
weighted.deviance.poisson(...)
## Generic S3 method
## for Poisson Deviance
deviance.poisson(...)

## Generic S3 method
## for weighted Poisson Deviance
weighted.deviance.poisson(...)

Arguments

...

Arguments passed on to deviance.poisson.numeric, weighted.deviance.poisson.numeric

actual,predicted: A pair of <double> vectors of length $n$ .
w: A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::deviance.poisson(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::deviance.poisson(
   actual    = actual_values, 
   predicted = predicted_values
)

Poisson Deviance

Description

Defensive measures

To guard against this, wrap deviance.poisson() in a "safe" validator that checks for NA values and matching length, for example:

safe_deviance.poisson <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  deviance.poisson(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'poisson.numeric'
deviance(actual, predicted, ...)
## S3 method for class 'poisson.numeric'
deviance(actual, predicted, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::deviance.poisson(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::deviance.poisson(
   actual    = actual_values, 
   predicted = predicted_values
)

Tweedie Deviance

Description

A generic S3 function to compute the tweedie deviance score for a regression model. This function dispatches to S3 methods in deviance.tweedie() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because deviance.tweedie() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap deviance.tweedie() in a "safe" validator that checks for NA values and matching length, for example:

safe_deviance.tweedie <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  deviance.tweedie(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Tweedie Deviance
deviance.tweedie(...)

## Generic S3 method
## for weighted Tweedie Deviance
weighted.deviance.tweedie(...)
## Generic S3 method
## for Tweedie Deviance
deviance.tweedie(...)

## Generic S3 method
## for weighted Tweedie Deviance
weighted.deviance.tweedie(...)

Arguments

...

Arguments passed on to deviance.tweedie.numeric, weighted.deviance.tweedie.numeric

actual,predicted

A pair of <double> vectors of length $n$ .

power

A <double> value, default = 2. Tweedie power parameter. Either power <= 0 or power >= 1.

The higher $power$ , the less weight is given to extreme deviations between actual and predicted values.

power < 0: Extreme stable distribution. Requires: predicted > 0.
power = 0: Normal distribution, output corresponds to mse(), actual and predicted can be any real numbers.
power = 1: Poisson distribution (deviance.poisson()). Requires: actual >= 0 and predicted > 0.
1 < power < 2: Compound Poisson distribution. Requires: actual >= 0 and predicted > 0.
power = 2: Gamma distribution (deviance.gamma()). Requires: actual > 0 and predicted > 0.
power = 3: Inverse Gaussian distribution. Requires: actual > 0 and predicted > 0.
otherwise: Positive stable distribution. Requires: actual > 0 and predicted > 0.

w

A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::deviance.tweedie(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::deviance.tweedie(
   actual    = actual_values, 
   predicted = predicted_values
)

Tweedie Deviance

Description

Defensive measures

To guard against this, wrap deviance.tweedie() in a "safe" validator that checks for NA values and matching length, for example:

safe_deviance.tweedie <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  deviance.tweedie(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'tweedie.numeric'
deviance(actual, predicted, power = 2, ...)
## S3 method for class 'tweedie.numeric'
deviance(actual, predicted, power = 2, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

power

A <double> value, default = 2. Tweedie power parameter. Either power <= 0 or power >= 1.

The higher $power$ , the less weight is given to extreme deviations between actual and predicted values.

power < 0: Extreme stable distribution. Requires: predicted > 0.
power = 0: Normal distribution, output corresponds to mse(), actual and predicted can be any real numbers.
power = 1: Poisson distribution (deviance.poisson()). Requires: actual >= 0 and predicted > 0.
1 < power < 2: Compound Poisson distribution. Requires: actual >= 0 and predicted > 0.
power = 2: Gamma distribution (deviance.gamma()). Requires: actual > 0 and predicted > 0.
power = 3: Inverse Gaussian distribution. Requires: actual > 0 and predicted > 0.
otherwise: Positive stable distribution. Requires: actual > 0 and predicted > 0.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::deviance.tweedie(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::deviance.tweedie(
   actual    = actual_values, 
   predicted = predicted_values
)

Diagnostic Odds Ratio

Description

A generic S3 function to compute the diagnostic odds ratio score for a classification model. This function dispatches to S3 methods in dor() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because dor() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap dor() in a "safe" validator that checks for NA values and matching length, for example:

safe_dor <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  dor(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate diagnostic odds ratio
## via S3 dispatching
dor(confusion_matrix)

## additional performance metrics
## below

The dor.factor() method calls cmatrix() internally, so explicitly invoking dor.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## Generic S3 method
## for Diagnostic Odds Ratio
dor(...)
## Generic S3 method
## for Diagnostic Odds Ratio
dor(...)

Arguments

...

Arguments passed on to dor.factor, weighted.dor.factor, dor.cmatrix

actual,predicted: A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.
w: A <double> vector of sample weights.
x: A confusion matrix created cmatrix().

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::dor(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::dor(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Diagnostic Odds Ratio

Description

Defensive measures

To guard against this, wrap dor() in a "safe" validator that checks for NA values and matching length, for example:

safe_dor <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  dor(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate diagnostic odds ratio
## via S3 dispatching
dor(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
dor(x, ...)
## S3 method for class 'cmatrix'
dor(x, ...)

Arguments

x

A confusion matrix created cmatrix().

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::dor(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::dor(confusion_matrix)

Diagnostic Odds Ratio

Description

Defensive measures

To guard against this, wrap dor() in a "safe" validator that checks for NA values and matching length, for example:

safe_dor <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  dor(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate diagnostic odds ratio
## via S3 dispatching
dor(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
dor(actual, predicted, ...)
## S3 method for class 'factor'
dor(actual, predicted, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::dor(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::dor(
   actual    = actual_classes, 
   predicted = predicted_classes
)

$f_{\beta}$

Description

A generic S3 function to compute the $f_{\beta}$ score for a classification model. This function dispatches to S3 methods in fbeta() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because fbeta() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap fbeta() in a "safe" validator that checks for NA values and matching length, for example:

safe_fbeta <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fbeta(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate \eqn{f_{\beta}}
## via S3 dispatching
fbeta(confusion_matrix)

## additional performance metrics
## below

The fbeta.factor() method calls cmatrix() internally, so explicitly invoking fbeta.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## Generic S3 method
## for \eqn{f_{\beta}}
fbeta(...)

## Generic S3 method
## for weighted \eqn{f_{\beta}}
weighted.fbeta(...)
## Generic S3 method
## for \eqn{f_{\beta}}
fbeta(...)

## Generic S3 method
## for weighted \eqn{f_{\beta}}
weighted.fbeta(...)

Arguments

...

Arguments passed on to fbeta.factor, weighted.fbeta.factor, fbeta.cmatrix

beta

A <double> vector of length $1$ (default: $1$ ).

actual,predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

w

A <double> vector of sample weights.

x

A confusion matrix created cmatrix().

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fbeta(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fbeta(
   actual    = actual_classes, 
   predicted = predicted_classes
)

$f_{\beta}$

Description

Defensive measures

To guard against this, wrap fbeta() in a "safe" validator that checks for NA values and matching length, for example:

safe_fbeta <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fbeta(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate \eqn{f_{\beta}}
## via S3 dispatching
fbeta(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
fbeta(x, beta = 1, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'cmatrix'
fbeta(x, beta = 1, estimator = 0L, na.rm = TRUE, ...)

Arguments

x

A confusion matrix created cmatrix().

beta

A <double> vector of length $1$ (default: $1$ ).

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::fbeta(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::fbeta(confusion_matrix)

$f_{\beta}$

Description

Defensive measures

To guard against this, wrap fbeta() in a "safe" validator that checks for NA values and matching length, for example:

safe_fbeta <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fbeta(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate \eqn{f_{\beta}}
## via S3 dispatching
fbeta(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
fbeta(actual, predicted, beta = 1, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'factor'
fbeta(actual, predicted, beta = 1, estimator = 0L, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

beta

A <double> vector of length $1$ (default: $1$ ).

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fbeta(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fbeta(
   actual    = actual_classes, 
   predicted = predicted_classes
)

False Discovery Rate

Description

A generic S3 function to compute the false discovery rate score for a classification model. This function dispatches to S3 methods in fdr() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because fdr() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap fdr() in a "safe" validator that checks for NA values and matching length, for example:

safe_fdr <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fdr(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate false discovery rate
## via S3 dispatching
fdr(confusion_matrix)

## additional performance metrics
## below

The fdr.factor() method calls cmatrix() internally, so explicitly invoking fdr.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## Generic S3 method
## for False Discovery Rate
fdr(...)

## Generic S3 method
## for weighted False Discovery Rate
weighted.fdr(...)
## Generic S3 method
## for False Discovery Rate
fdr(...)

## Generic S3 method
## for weighted False Discovery Rate
weighted.fdr(...)

Arguments

...

Arguments passed on to fdr.factor, weighted.fdr.factor, fdr.cmatrix

actual,predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

w

A <double> vector of sample weights.

x

A confusion matrix created cmatrix().

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fdr(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fdr(
   actual    = actual_classes, 
   predicted = predicted_classes
)

False Discovery Rate

Description

Defensive measures

To guard against this, wrap fdr() in a "safe" validator that checks for NA values and matching length, for example:

safe_fdr <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fdr(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate false discovery rate
## via S3 dispatching
fdr(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
fdr(x, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'cmatrix'
fdr(x, estimator = 0L, na.rm = TRUE, ...)

Arguments

x

A confusion matrix created cmatrix().

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::fdr(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::fdr(confusion_matrix)

False Discovery Rate

Description

Defensive measures

To guard against this, wrap fdr() in a "safe" validator that checks for NA values and matching length, for example:

safe_fdr <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fdr(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate false discovery rate
## via S3 dispatching
fdr(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
fdr(actual, predicted, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'factor'
fdr(actual, predicted, estimator = 0L, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fdr(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fdr(
   actual    = actual_classes, 
   predicted = predicted_classes
)

False Omission Rate

Description

A generic S3 function to compute the false omission rate score for a classification model. This function dispatches to S3 methods in fer() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because fer() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap fer() in a "safe" validator that checks for NA values and matching length, for example:

safe_fer <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fer(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate false omission rate
## via S3 dispatching
fer(confusion_matrix)

## additional performance metrics
## below

The fer.factor() method calls cmatrix() internally, so explicitly invoking fer.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## Generic S3 method
## for False Omission Rate
fer(...)

## Generic S3 method
## for weighted False Omission Rate
weighted.fer(...)
## Generic S3 method
## for False Omission Rate
fer(...)

## Generic S3 method
## for weighted False Omission Rate
weighted.fer(...)

Arguments

...

Arguments passed on to fer.factor, weighted.fer.factor, fer.cmatrix

actual,predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

w

A <double> vector of sample weights.

x

A confusion matrix created cmatrix().

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fer(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fer(
   actual    = actual_classes, 
   predicted = predicted_classes
)

False Omission Rate

Description

Defensive measures

To guard against this, wrap fer() in a "safe" validator that checks for NA values and matching length, for example:

safe_fer <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fer(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate false omission rate
## via S3 dispatching
fer(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
fer(x, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'cmatrix'
fer(x, estimator = 0L, na.rm = TRUE, ...)

Arguments

x

A confusion matrix created cmatrix().

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::fer(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::fer(confusion_matrix)

False Omission Rate

Description

Defensive measures

To guard against this, wrap fer() in a "safe" validator that checks for NA values and matching length, for example:

safe_fer <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fer(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate false omission rate
## via S3 dispatching
fer(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
fer(actual, predicted, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'factor'
fer(actual, predicted, estimator = 0L, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fer(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fer(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Fowlkes Mallows Index

Description

A generic S3 function to compute the fowlkes mallows index score for a classification model. This function dispatches to S3 methods in fmi() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because fmi() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap fmi() in a "safe" validator that checks for NA values and matching length, for example:

safe_fmi <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fmi(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate fowlkes mallows index
## via S3 dispatching
fmi(confusion_matrix)

## additional performance metrics
## below

The fmi.factor() method calls cmatrix() internally, so explicitly invoking fmi.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## Generic S3 method
## for Fowlkes Mallows Index
fmi(...)

## Generic S3 method
## for weighted Fowlkes Mallows Index
weighted.fmi(...)
## Generic S3 method
## for Fowlkes Mallows Index
fmi(...)

## Generic S3 method
## for weighted Fowlkes Mallows Index
weighted.fmi(...)

Arguments

...

Arguments passed on to fmi.factor, weighted.fmi.factor, fmi.cmatrix

actual,predicted: A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.
w: A <double> vector of sample weights.
x: A confusion matrix created cmatrix().

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fmi(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fmi(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Fowlkes Mallows Index

Description

Defensive measures

To guard against this, wrap fmi() in a "safe" validator that checks for NA values and matching length, for example:

safe_fmi <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fmi(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate fowlkes mallows index
## via S3 dispatching
fmi(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
fmi(x, ...)
## S3 method for class 'cmatrix'
fmi(x, ...)

Arguments

x

A confusion matrix created cmatrix().

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::fmi(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::fmi(confusion_matrix)

Fowlkes Mallows Index

Description

Defensive measures

To guard against this, wrap fmi() in a "safe" validator that checks for NA values and matching length, for example:

safe_fmi <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fmi(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate fowlkes mallows index
## via S3 dispatching
fmi(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
fmi(actual, predicted, ...)
## S3 method for class 'factor'
fmi(actual, predicted, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fmi(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fmi(
   actual    = actual_classes, 
   predicted = predicted_classes
)

False Positive Rate

Description

A generic S3 function to compute the false positive rate score for a classification model. This function dispatches to S3 methods in fpr() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because fpr() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap fpr() in a "safe" validator that checks for NA values and matching length, for example:

safe_fpr <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fpr(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate false positive rate
## via S3 dispatching
fpr(confusion_matrix)

## additional performance metrics
## below

The fpr.factor() method calls cmatrix() internally, so explicitly invoking fpr.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## Generic S3 method
## for False Positive Rate
fpr(...)

## Generic S3 method
## for weighted False Positive Rate
weighted.fpr(...)
## Generic S3 method
## for False Positive Rate
fpr(...)

## Generic S3 method
## for weighted False Positive Rate
weighted.fpr(...)

Arguments

...

Arguments passed on to fpr.factor, weighted.fpr.factor, fpr.cmatrix

actual,predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

w

A <double> vector of sample weights.

x

A confusion matrix created cmatrix().

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The false positive rate has other names depending on research field:

Fallout, fallout()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fpr(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fpr(
   actual    = actual_classes, 
   predicted = predicted_classes
)

False Positive Rate

Description

Defensive measures

To guard against this, wrap fpr() in a "safe" validator that checks for NA values and matching length, for example:

safe_fpr <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fpr(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate false positive rate
## via S3 dispatching
fpr(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
fpr(x, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'cmatrix'
fpr(x, estimator = 0L, na.rm = TRUE, ...)

Arguments

x

A confusion matrix created cmatrix().

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The false positive rate has other names depending on research field:

Fallout, fallout()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::fpr(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::fpr(confusion_matrix)

False Positive Rate

Description

Defensive measures

To guard against this, wrap fpr() in a "safe" validator that checks for NA values and matching length, for example:

safe_fpr <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fpr(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate false positive rate
## via S3 dispatching
fpr(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
fpr(actual, predicted, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'factor'
fpr(actual, predicted, estimator = 0L, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The false positive rate has other names depending on research field:

Fallout, fallout()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fpr(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fpr(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Geometric Mean Squared Error

Description

A generic S3 function to compute the geometric mean squared error score for a regression model. This function dispatches to S3 methods in gmse() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because gmse() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap gmse() in a "safe" validator that checks for NA values and matching length, for example:

safe_gmse <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  gmse(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Geometric Mean Squared Error
gmse(...)

## Generic S3 method
## for weighted Geometric Mean Squared Error
weighted.gmse(...)
## Generic S3 method
## for Geometric Mean Squared Error
gmse(...)

## Generic S3 method
## for weighted Geometric Mean Squared Error
weighted.gmse(...)

Arguments

...

Arguments passed on to gmse.numeric, weighted.gmse.numeric

actual,predicted: A pair of <double> vectors of length $n$ .
w: A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::gmse(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::gmse(
   actual    = actual_values, 
   predicted = predicted_values
)

Geometric Mean Squared Error

Description

Defensive measures

To guard against this, wrap gmse() in a "safe" validator that checks for NA values and matching length, for example:

safe_gmse <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  gmse(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
gmse(actual, predicted, ...)
## S3 method for class 'numeric'
gmse(actual, predicted, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::gmse(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::gmse(
   actual    = actual_values, 
   predicted = predicted_values
)

Hamming Loss

Description

A generic S3 function to compute the hamming loss score for a classification model. This function dispatches to S3 methods in hammingloss() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because hammingloss() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap hammingloss() in a "safe" validator that checks for NA values and matching length, for example:

safe_hammingloss <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  hammingloss(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate hamming loss
## via S3 dispatching
hammingloss(confusion_matrix)

## additional performance metrics
## below

The hammingloss.factor() method calls cmatrix() internally, so explicitly invoking hammingloss.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## Generic S3 method
## for Hamming Loss
hammingloss(...)

## Generic S3 method
## for weighted Hamming Loss
weighted.hammingloss(...)
## Generic S3 method
## for Hamming Loss
hammingloss(...)

## Generic S3 method
## for weighted Hamming Loss
weighted.hammingloss(...)

Arguments

...

Arguments passed on to hammingloss.factor, weighted.hammingloss.factor, hammingloss.cmatrix

actual,predicted: A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.
w: A <double> vector of sample weights.
x: A confusion matrix created cmatrix().

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::hammingloss(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::hammingloss(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Hamming Loss

Description

Defensive measures

To guard against this, wrap hammingloss() in a "safe" validator that checks for NA values and matching length, for example:

safe_hammingloss <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  hammingloss(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate hamming loss
## via S3 dispatching
hammingloss(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
hammingloss(x, ...)
## S3 method for class 'cmatrix'
hammingloss(x, ...)

Arguments

x

A confusion matrix created cmatrix().

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::hammingloss(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::hammingloss(confusion_matrix)

Hamming Loss

Description

Defensive measures

To guard against this, wrap hammingloss() in a "safe" validator that checks for NA values and matching length, for example:

safe_hammingloss <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  hammingloss(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate hamming loss
## via S3 dispatching
hammingloss(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
hammingloss(actual, predicted, ...)
## S3 method for class 'factor'
hammingloss(actual, predicted, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::hammingloss(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::hammingloss(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Huber Loss

Description

A generic S3 function to compute the huber loss score for a regression model. This function dispatches to S3 methods in huberloss() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because huberloss() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap huberloss() in a "safe" validator that checks for NA values and matching length, for example:

safe_huberloss <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  huberloss(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Huber Loss
huberloss(...)

## Generic S3 method
## for weighted Huber Loss
weighted.huberloss(...)
## Generic S3 method
## for Huber Loss
huberloss(...)

## Generic S3 method
## for weighted Huber Loss
weighted.huberloss(...)

Arguments

...

Arguments passed on to huberloss.numeric, weighted.huberloss.numeric

actual,predicted: A pair of <double> vectors of length $n$ .
delta: A <double>-vector of length $1$ (default: $1$ ). The threshold value for switch between functions (see calculation).
w: A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::huberloss(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::huberloss(
   actual    = actual_values, 
   predicted = predicted_values
)

Huber Loss

Description

Defensive measures

To guard against this, wrap huberloss() in a "safe" validator that checks for NA values and matching length, for example:

safe_huberloss <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  huberloss(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
huberloss(actual, predicted, delta = 1, ...)
## S3 method for class 'numeric'
huberloss(actual, predicted, delta = 1, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

delta

A <double>-vector of length $1$ (default: $1$ ). The threshold value for switch between functions (see calculation).

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::huberloss(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::huberloss(
   actual    = actual_values, 
   predicted = predicted_values
)

Jaccard Index

Description

A generic S3 function to compute the jaccard index score for a classification model. This function dispatches to S3 methods in jaccard() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because jaccard() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap jaccard() in a "safe" validator that checks for NA values and matching length, for example:

safe_jaccard <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  jaccard(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate jaccard index
## via S3 dispatching
jaccard(confusion_matrix)

## additional performance metrics
## below

The jaccard.factor() method calls cmatrix() internally, so explicitly invoking jaccard.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## Generic S3 method
## for Jaccard Index
jaccard(...)

## Generic S3 method
## for weighted Jaccard Index
weighted.jaccard(...)
## Generic S3 method
## for Jaccard Index
jaccard(...)

## Generic S3 method
## for weighted Jaccard Index
weighted.jaccard(...)

Arguments

...

Arguments passed on to jaccard.factor, weighted.jaccard.factor, jaccard.cmatrix

actual,predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

w

A <double> vector of sample weights.

x

A confusion matrix created cmatrix().

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The specificity has other names depending on research field:

Critical Success Index, csi()
Threat Score, tscore()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::jaccard(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::jaccard(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Jaccard Index

Description

Defensive measures

To guard against this, wrap jaccard() in a "safe" validator that checks for NA values and matching length, for example:

safe_jaccard <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  jaccard(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate jaccard index
## via S3 dispatching
jaccard(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
jaccard(x, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'cmatrix'
jaccard(x, estimator = 0L, na.rm = TRUE, ...)

Arguments

x

A confusion matrix created cmatrix().

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The specificity has other names depending on research field:

Critical Success Index, csi()
Threat Score, tscore()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::jaccard(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::jaccard(confusion_matrix)

Jaccard Index

Description

Defensive measures

To guard against this, wrap jaccard() in a "safe" validator that checks for NA values and matching length, for example:

safe_jaccard <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  jaccard(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate jaccard index
## via S3 dispatching
jaccard(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
jaccard(actual, predicted, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'factor'
jaccard(actual, predicted, estimator = 0L, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The specificity has other names depending on research field:

Critical Success Index, csi()
Threat Score, tscore()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::jaccard(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::jaccard(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Logarithmic Loss

Description

A generic S3 function to compute the logarithmic loss score for a classification model. This function dispatches to S3 methods in logloss() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because logloss() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap logloss() in a "safe" validator that checks for NA values and matching length, for example:

safe_logloss <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  logloss(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Logarithmic Loss
logloss(...)

## Generic S3 method
## for weighted Logarithmic Loss
weighted.logloss(...)
## Generic S3 method
## for Logarithmic Loss
logloss(...)

## Generic S3 method
## for weighted Logarithmic Loss
weighted.logloss(...)

Arguments

...

Arguments passed on to logloss.integer, logloss.factor, weighted.logloss.integer, weighted.logloss.factor

actual: A vector length $n$ , and $k$ levels. Can be of integer or factor.
response: A $n \times k$ <double>-matrix of predicted probabilities. The $i$ -th row should sum to 1 (i.e., a valid probability distribution over the $k$ classes). The first column corresponds to the first factor level in actual, the second column to the second factor level, and so on.
normalize: A <logical>-value (default: TRUE). If TRUE, the mean cross-entropy across all observations is returned; otherwise, the sum of cross-entropies is returned.
w: A <double> vector of sample weights.

Value

A <double>

References

MacKay, David JC. Information theory, inference and learning algorithms. Cambridge university press, 2003.

Kramer, Oliver, and Oliver Kramer. "Scikit-learn." Machine learning for evolution strategies (2016): 45-53.

Virtanen, Pauli, et al. "SciPy 1.0: f'undamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted response
## probabilities
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

response <- runif(n = 1e3)

## Logloss
SLmetrics::logloss(
   actual    = actual_classes, 
   response  = cbind(
     response,
     1 - response
   )
)

## Generate observed
## frequencies 
actual_frequency <- sample(10L:100L, size = 1e3, replace = TRUE)

## Poisson Logloss
SLmetrics::logloss(
   actual    = actual_frequency, 
   response  = response
)





## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted response
## probabilities
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

response <- runif(n = 1e3)

## Logloss
SLmetrics::logloss(
   actual    = actual_classes, 
   response  = cbind(
     response,
     1 - response
   )
)

## Generate observed
## frequencies 
actual_frequency <- sample(10L:100L, size = 1e3, replace = TRUE)

## Poisson Logloss
SLmetrics::logloss(
   actual    = actual_frequency, 
   response  = response
)

Logarithmic Loss

Description

Defensive measures

To guard against this, wrap logloss() in a "safe" validator that checks for NA values and matching length, for example:

safe_logloss <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  logloss(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'factor'
logloss(actual, response, normalize = TRUE, ...)
## S3 method for class 'factor'
logloss(actual, response, normalize = TRUE, ...)

Arguments

actual

A vector length $n$ , and $k$ levels. Can be of integer or factor.

response

normalize

A <logical>-value (default: TRUE). If TRUE, the mean cross-entropy across all observations is returned; otherwise, the sum of cross-entropies is returned.

...

Arguments passed into other methods.

Value

A <double>

References

MacKay, David JC. Information theory, inference and learning algorithms. Cambridge university press, 2003.

Kramer, Oliver, and Oliver Kramer. "Scikit-learn." Machine learning for evolution strategies (2016): 45-53.

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted response
## probabilities
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

response <- runif(n = 1e3)

## Evaluate performance
SLmetrics::logloss(
   actual    = actual_classes, 
   response  = cbind(
     response,
     1 - response
   )
)

## Generate observed
## frequencies 
actual_frequency <- sample(10L:100L, size = 1e3, replace = TRUE)

SLmetrics::logloss(
   actual    = actual_frequency, 
   response  = response
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted response
## probabilities
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

response <- runif(n = 1e3)

## Evaluate performance
SLmetrics::logloss(
   actual    = actual_classes, 
   response  = cbind(
     response,
     1 - response
   )
)

## Generate observed
## frequencies 
actual_frequency <- sample(10L:100L, size = 1e3, replace = TRUE)

SLmetrics::logloss(
   actual    = actual_frequency, 
   response  = response
)

Logarithmic Loss

Description

Defensive measures

To guard against this, wrap logloss() in a "safe" validator that checks for NA values and matching length, for example:

safe_logloss <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  logloss(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'integer'
logloss(actual, response, normalize = TRUE, ...)
## S3 method for class 'integer'
logloss(actual, response, normalize = TRUE, ...)

Arguments

actual

A vector length $n$ , and $k$ levels. Can be of integer or factor.

response

normalize

A <logical>-value (default: TRUE). If TRUE, the mean cross-entropy across all observations is returned; otherwise, the sum of cross-entropies is returned.

...

Arguments passed into other methods.

Value

A <double>

References

MacKay, David JC. Information theory, inference and learning algorithms. Cambridge university press, 2003.

Kramer, Oliver, and Oliver Kramer. "Scikit-learn." Machine learning for evolution strategies (2016): 45-53.

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted response
## probabilities
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

response <- runif(n = 1e3)

## Evaluate performance
SLmetrics::logloss(
   actual    = actual_classes, 
   response  = cbind(
     response,
     1 - response
   )
)

## Generate observed
## frequencies 
actual_frequency <- sample(10L:100L, size = 1e3, replace = TRUE)

SLmetrics::logloss(
   actual    = actual_frequency, 
   response  = response
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted response
## probabilities
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

response <- runif(n = 1e3)

## Evaluate performance
SLmetrics::logloss(
   actual    = actual_classes, 
   response  = cbind(
     response,
     1 - response
   )
)

## Generate observed
## frequencies 
actual_frequency <- sample(10L:100L, size = 1e3, replace = TRUE)

SLmetrics::logloss(
   actual    = actual_frequency, 
   response  = response
)

Mean Arctangent Absolute Percentage Error

Description

A generic S3 function to compute the mean arctangent absolute percentage error score for a regression model. This function dispatches to S3 methods in maape() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because maape() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap maape() in a "safe" validator that checks for NA values and matching length, for example:

safe_maape <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  maape(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Mean Arctangent Absolute Percentage Error
maape(...)

## Generic S3 method
## for weighted Mean Arctangent Absolute Percentage Error
weighted.maape(...)
## Generic S3 method
## for Mean Arctangent Absolute Percentage Error
maape(...)

## Generic S3 method
## for weighted Mean Arctangent Absolute Percentage Error
weighted.maape(...)

Arguments

...

Arguments passed on to maape.numeric, weighted.maape.numeric

actual,predicted: A pair of <double> vectors of length $n$ .
w: A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::maape(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::maape(
   actual    = actual_values, 
   predicted = predicted_values
)

Mean Arctangent Absolute Percentage Error

Description

Defensive measures

To guard against this, wrap maape() in a "safe" validator that checks for NA values and matching length, for example:

safe_maape <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  maape(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
maape(actual, predicted, ...)
## S3 method for class 'numeric'
maape(actual, predicted, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::maape(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::maape(
   actual    = actual_values, 
   predicted = predicted_values
)

Mean Absolute Error

Description

A generic S3 function to compute the mean absolute error score for a regression model. This function dispatches to S3 methods in mae() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because mae() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap mae() in a "safe" validator that checks for NA values and matching length, for example:

safe_mae <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  mae(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Mean Absolute Error
mae(...)

## Generic S3 method
## for weighted Mean Absolute Error
weighted.mae(...)
## Generic S3 method
## for Mean Absolute Error
mae(...)

## Generic S3 method
## for weighted Mean Absolute Error
weighted.mae(...)

Arguments

...

Arguments passed on to mae.numeric, weighted.mae.numeric

actual,predicted: A pair of <double> vectors of length $n$ .
w: A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::mae(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::mae(
   actual    = actual_values, 
   predicted = predicted_values
)

Mean Absolute Error

Description

Defensive measures

To guard against this, wrap mae() in a "safe" validator that checks for NA values and matching length, for example:

safe_mae <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  mae(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
mae(actual, predicted, ...)
## S3 method for class 'numeric'
mae(actual, predicted, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::mae(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::mae(
   actual    = actual_values, 
   predicted = predicted_values
)

Mean Absolute Percentage Error

Description

A generic S3 function to compute the mean absolute percentage error score for a regression model. This function dispatches to S3 methods in mape() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because mape() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap mape() in a "safe" validator that checks for NA values and matching length, for example:

safe_mape <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  mape(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Mean Absolute Percentage Error
mape(...)

## Generic S3 method
## for weighted Mean Absolute Percentage Error
weighted.mape(...)
## Generic S3 method
## for Mean Absolute Percentage Error
mape(...)

## Generic S3 method
## for weighted Mean Absolute Percentage Error
weighted.mape(...)

Arguments

...

Arguments passed on to mape.numeric, weighted.mape.numeric

actual,predicted: A pair of <double> vectors of length $n$ .
w: A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::mape(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::mape(
   actual    = actual_values, 
   predicted = predicted_values
)

Mean Absolute Percentage Error

Description

Defensive measures

To guard against this, wrap mape() in a "safe" validator that checks for NA values and matching length, for example:

safe_mape <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  mape(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
mape(actual, predicted, ...)
## S3 method for class 'numeric'
mape(actual, predicted, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::mape(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::mape(
   actual    = actual_values, 
   predicted = predicted_values
)

Matthews Correlation Coefficient

Description

A generic S3 function to compute the matthews correlation coefficient score for a classification model. This function dispatches to S3 methods in mcc() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because mcc() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap mcc() in a "safe" validator that checks for NA values and matching length, for example:

safe_mcc <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  mcc(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate matthews correlation coefficient
## via S3 dispatching
mcc(confusion_matrix)

## additional performance metrics
## below

The mcc.factor() method calls cmatrix() internally, so explicitly invoking mcc.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## Generic S3 method
## for Matthews Correlation Coefficient
mcc(...)

## Generic S3 method
## for weighted Matthews Correlation Coefficient
weighted.mcc(...)
## Generic S3 method
## for Matthews Correlation Coefficient
mcc(...)

## Generic S3 method
## for weighted Matthews Correlation Coefficient
weighted.mcc(...)

Arguments

...

Arguments passed on to mcc.factor, weighted.mcc.factor, mcc.cmatrix

actual,predicted: A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.
w: A <double> vector of sample weights.
x: A confusion matrix created cmatrix().

Value

A <double>-value

Other names

The Matthews Correlation Coefficient has other names depending on research field:

$\phi$ -coefficient, phi()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::mcc(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::mcc(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Matthews Correlation Coefficient

Description

Defensive measures

To guard against this, wrap mcc() in a "safe" validator that checks for NA values and matching length, for example:

safe_mcc <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  mcc(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate matthews correlation coefficient
## via S3 dispatching
mcc(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
mcc(x, ...)
## S3 method for class 'cmatrix'
mcc(x, ...)

Arguments

x

A confusion matrix created cmatrix().

...

Arguments passed into other methods.

Value

A <double>-value

Other names

The Matthews Correlation Coefficient has other names depending on research field:

$\phi$ -coefficient, phi()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::mcc(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::mcc(confusion_matrix)

Matthews Correlation Coefficient

Description

Defensive measures

To guard against this, wrap mcc() in a "safe" validator that checks for NA values and matching length, for example:

safe_mcc <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  mcc(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate matthews correlation coefficient
## via S3 dispatching
mcc(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
mcc(actual, predicted, ...)
## S3 method for class 'factor'
mcc(actual, predicted, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

...

Arguments passed into other methods.

Value

A <double>-value

Other names

The Matthews Correlation Coefficient has other names depending on research field:

$\phi$ -coefficient, phi()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::mcc(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::mcc(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Mean Percentage Error

Description

A generic S3 function to compute the mean percentage error score for a regression model. This function dispatches to S3 methods in mpe() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because mpe() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap mpe() in a "safe" validator that checks for NA values and matching length, for example:

safe_mpe <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  mpe(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Mean Percentage Error
mpe(...)

## Generic S3 method
## for weighted Mean Percentage Error
weighted.mpe(...)
## Generic S3 method
## for Mean Percentage Error
mpe(...)

## Generic S3 method
## for weighted Mean Percentage Error
weighted.mpe(...)

Arguments

...

Arguments passed on to mpe.numeric, weighted.mpe.numeric

actual,predicted: A pair of <double> vectors of length $n$ .
w: A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::mpe(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::mpe(
   actual    = actual_values, 
   predicted = predicted_values
)

Mean Percentage Error

Description

Defensive measures

To guard against this, wrap mpe() in a "safe" validator that checks for NA values and matching length, for example:

safe_mpe <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  mpe(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
mpe(actual, predicted, ...)
## S3 method for class 'numeric'
mpe(actual, predicted, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::mpe(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::mpe(
   actual    = actual_values, 
   predicted = predicted_values
)

Mean Squared Error

Description

A generic S3 function to compute the mean squared error score for a regression model. This function dispatches to S3 methods in mse() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because mse() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap mse() in a "safe" validator that checks for NA values and matching length, for example:

safe_mse <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  mse(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Mean Squared Error
mse(...)

## Generic S3 method
## for weighted Mean Squared Error
weighted.mse(...)
## Generic S3 method
## for Mean Squared Error
mse(...)

## Generic S3 method
## for weighted Mean Squared Error
weighted.mse(...)

Arguments

...

Arguments passed on to mse.numeric, weighted.mse.numeric

actual,predicted: A pair of <double> vectors of length $n$ .
w: A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::mse(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::mse(
   actual    = actual_values, 
   predicted = predicted_values
)

Mean Squared Error

Description

Defensive measures

To guard against this, wrap mse() in a "safe" validator that checks for NA values and matching length, for example:

safe_mse <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  mse(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
mse(actual, predicted, ...)
## S3 method for class 'numeric'
mse(actual, predicted, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::mse(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::mse(
   actual    = actual_values, 
   predicted = predicted_values
)

Negative Likelihood Ratio

Description

A generic S3 function to compute the negative likelihood ratio score for a classification model. This function dispatches to S3 methods in nlr() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because nlr() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap nlr() in a "safe" validator that checks for NA values and matching length, for example:

safe_nlr <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  nlr(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate negative likelihood ratio
## via S3 dispatching
nlr(confusion_matrix)

## additional performance metrics
## below

The nlr.factor() method calls cmatrix() internally, so explicitly invoking nlr.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## Generic S3 method
## for Negative Likelihood Ratio
nlr(...)

## Generic S3 method
## for weighted Negative Likelihood Ratio
weighted.nlr(...)
## Generic S3 method
## for Negative Likelihood Ratio
nlr(...)

## Generic S3 method
## for weighted Negative Likelihood Ratio
weighted.nlr(...)

Arguments

...

Arguments passed on to nlr.factor, weighted.nlr.factor, nlr.cmatrix

actual,predicted: A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.
w: A <double> vector of sample weights.
x: A confusion matrix created cmatrix().

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::nlr(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::nlr(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Negative Likelihood Ratio

Description

Defensive measures

To guard against this, wrap nlr() in a "safe" validator that checks for NA values and matching length, for example:

safe_nlr <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  nlr(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate negative likelihood ratio
## via S3 dispatching
nlr(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
nlr(x, ...)
## S3 method for class 'cmatrix'
nlr(x, ...)

Arguments

x

A confusion matrix created cmatrix().

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::nlr(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::nlr(confusion_matrix)

Negative Likelihood Ratio

Description

Defensive measures

To guard against this, wrap nlr() in a "safe" validator that checks for NA values and matching length, for example:

safe_nlr <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  nlr(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate negative likelihood ratio
## via S3 dispatching
nlr(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
nlr(actual, predicted, ...)
## S3 method for class 'factor'
nlr(actual, predicted, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::nlr(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::nlr(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Negative Predictive Value

Description

A generic S3 function to compute the negative predictive value score for a classification model. This function dispatches to S3 methods in npv() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because npv() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap npv() in a "safe" validator that checks for NA values and matching length, for example:

safe_npv <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  npv(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate negative predictive value
## via S3 dispatching
npv(confusion_matrix)

## additional performance metrics
## below

The npv.factor() method calls cmatrix() internally, so explicitly invoking npv.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## Generic S3 method
## for Negative Predictive Value
npv(...)

## Generic S3 method
## for weighted Negative Predictive Value
weighted.npv(...)
## Generic S3 method
## for Negative Predictive Value
npv(...)

## Generic S3 method
## for weighted Negative Predictive Value
weighted.npv(...)

Arguments

...

Arguments passed on to npv.factor, weighted.npv.factor, npv.cmatrix

actual,predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

w

A <double> vector of sample weights.

x

A confusion matrix created cmatrix().

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::npv(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::npv(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Negative Predictive Value

Description

Defensive measures

To guard against this, wrap npv() in a "safe" validator that checks for NA values and matching length, for example:

safe_npv <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  npv(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate negative predictive value
## via S3 dispatching
npv(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
npv(x, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'cmatrix'
npv(x, estimator = 0L, na.rm = TRUE, ...)

Arguments

x

A confusion matrix created cmatrix().

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::npv(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::npv(confusion_matrix)

Negative Predictive Value

Description

Defensive measures

To guard against this, wrap npv() in a "safe" validator that checks for NA values and matching length, for example:

safe_npv <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  npv(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate negative predictive value
## via S3 dispatching
npv(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
npv(actual, predicted, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'factor'
npv(actual, predicted, estimator = 0L, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::npv(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::npv(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Obesity levels dataset

Description

This dataset is used to estimate obesity levels based on eating habits and physical condition. The data originates from the UCI Machine Learning Repository and has been preprocessed to include both predictors and a target variable.

The dataset is provided as a list with two components:

features

A data frame containing various predictors related to lifestyle, eating habits, and physical condition. The variables include:

age: The age of the individual in years.
height: The height of the individual in meters.
family_history_with_overweight: Binary variable indicating whether the individual has a family history of overweight (1 = yes, 0 = no).
favc: Binary variable indicating whether the individual frequently consumes high-calorie foods (1 = yes, 0 = no).
fcvc: The frequency of consumption of vegetables in meals.
ncp: The number of main meals consumed per day.
caec: Categorical variable indicating the frequency of consumption of food between meals. Typical levels include "no", "sometimes", "frequently", and "always".
smoke: Binary variable indicating whether the individual smokes (1 = yes, 0 = no).
ch2o: Daily water consumption (typically in liters).
scc: Binary variable indicating whether the individual monitors calorie consumption (1 = yes, 0 = no).
faf: The frequency of physical activity.
tue: The time spent using electronic devices (e.g., screen time in hours).
calc: Categorical variable indicating the frequency of alcohol consumption. Typical levels include "no", "sometimes", "frequently", and "always".
male: Binary variable indicating the gender of the individual (1 = male, 0 = female).

target

A list containing two elements:

regression: A numeric vector representing the weight of the individual (used as the regression target).
class: A factor indicating the obesity level classification. The levels are derived from the original nobeyesdad variable in the dataset.

Usage

data(obesity)
data(obesity)

Format

A list with two components:

features: A data frame containing various predictors related to eating habits, physical condition, and lifestyle.
target: A list with two elements: regression (weight in kilograms) and class (obesity level classification).

References

Palechor, Fabio Mendoza, and Alexis De la Hoz Manotas. "Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from Colombia, Peru and Mexico." Data in brief 25 (2019): 104344.

Control OpenMP

Description

Enable or disable OpenMP parallelization for computations.

Disclaimer

This toggle is a brute-force implementation and does not guard against data races or nested parallel regions. Nested OpenMP regions can introduce subtle race conditions if multiple layers of parallelism access shared data concurrently. If you combine this package’s OpenMP switch with other parallel machine-learning routines, you may encounter undefined behavior.

Usage

## enable OpenMP
openmp.on()

## disable OpenMP
openmp.off()

## set number of threads
openmp.threads(threads)
## enable OpenMP
openmp.on()

## disable OpenMP
openmp.off()

## set number of threads
openmp.threads(threads)

Arguments

threads

A positive <integer>-value (Default: None). If threads is missing, the openmp.threads() returns the number of available threads. If NULL all available threads will be used.

Value

If OpenMP is unavailable, the function returns NULL.

Examples

## Not run: 
## enable OpenMP
SLmetrics::openmp.on()

## disable OpenMP
SLmetrics::openmp.off()

## available threads
SLmetrics::openmp.threads()

## set number of threads
SLmetrics::openmp.threads(2)

## End(Not run)


## Not run: 
## enable OpenMP
SLmetrics::openmp.on()

## disable OpenMP
SLmetrics::openmp.off()

## available threads
SLmetrics::openmp.threads()

## set number of threads
SLmetrics::openmp.threads(2)

## End(Not run)

Pinball Loss

Description

A generic S3 function to compute the pinball loss score for a regression model. This function dispatches to S3 methods in pinball() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because pinball() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap pinball() in a "safe" validator that checks for NA values and matching length, for example:

safe_pinball <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  pinball(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Pinball Loss
pinball(...)

## Generic S3 method
## for weighted Pinball Loss
weighted.pinball(...)
## Generic S3 method
## for Pinball Loss
pinball(...)

## Generic S3 method
## for weighted Pinball Loss
weighted.pinball(...)

Arguments

...

Arguments passed on to pinball.numeric, weighted.pinball.numeric

actual,predicted: A pair of <double> vectors of length $n$ .
alpha: A <double>-value of length $1$ (default: $0.5$ ). The slope of the pinball loss function.
deviance: A <logical>-value of length 1 (default: FALSE). If TRUE the function returns the $D^2$ loss.
w: A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::pinball(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::pinball(
   actual    = actual_values, 
   predicted = predicted_values
)

Pinball Loss

Description

Defensive measures

To guard against this, wrap pinball() in a "safe" validator that checks for NA values and matching length, for example:

safe_pinball <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  pinball(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
pinball(actual, predicted, alpha = 0.5, deviance = FALSE, ...)
## S3 method for class 'numeric'
pinball(actual, predicted, alpha = 0.5, deviance = FALSE, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

alpha

A <double>-value of length $1$ (default: $0.5$ ). The slope of the pinball loss function.

deviance

A <logical>-value of length 1 (default: FALSE). If TRUE the function returns the $D^2$ loss.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::pinball(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::pinball(
   actual    = actual_values, 
   predicted = predicted_values
)

Positive Likelihood Ratio

Description

A generic S3 function to compute the positive likelihood ratio score for a classification model. This function dispatches to S3 methods in plr() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because plr() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap plr() in a "safe" validator that checks for NA values and matching length, for example:

safe_plr <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  plr(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate positive likelihood ratio
## via S3 dispatching
plr(confusion_matrix)

## additional performance metrics
## below

The plr.factor() method calls cmatrix() internally, so explicitly invoking plr.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## Generic S3 method
## for Positive Likelihood Ratio
plr(...)

## Generic S3 method
## for weighted Positive Likelihood Ratio
weighted.plr(...)

## Generic S3 method
## for weighted Diagnostic Odds Ratio
weighted.plr(...)
## Generic S3 method
## for Positive Likelihood Ratio
plr(...)

## Generic S3 method
## for weighted Positive Likelihood Ratio
weighted.plr(...)

## Generic S3 method
## for weighted Diagnostic Odds Ratio
weighted.plr(...)

Arguments

...

Arguments passed on to plr.factor, weighted.plr.factor, plr.cmatrix

actual,predicted: A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.
w: A <double> vector of sample weights.
x: A confusion matrix created cmatrix().

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::plr(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::plr(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Positive Likelihood Ratio

Description

Defensive measures

To guard against this, wrap plr() in a "safe" validator that checks for NA values and matching length, for example:

safe_plr <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  plr(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate positive likelihood ratio
## via S3 dispatching
plr(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
plr(x, ...)
## S3 method for class 'cmatrix'
plr(x, ...)

Arguments

x

A confusion matrix created cmatrix().

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::plr(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::plr(confusion_matrix)

Positive Likelihood Ratio

Description

Defensive measures

To guard against this, wrap plr() in a "safe" validator that checks for NA values and matching length, for example:

safe_plr <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  plr(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate positive likelihood ratio
## via S3 dispatching
plr(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
plr(actual, predicted, ...)
## S3 method for class 'factor'
plr(actual, predicted, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::plr(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::plr(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Precision Recall Curve

Description

A generic S3 function to compute the precision recall curve score for a classification model. This function dispatches to S3 methods in pr.curve() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because pr.curve() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap pr.curve() in a "safe" validator that checks for NA values and matching length, for example:

safe_pr.curve <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  pr.curve(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Area under the curve

Use auc.pr.curve for calculating the area under the curve directly.

Efficient multi-metric evaluation

## presort response
## probabilities
indices <- preorder(response, decreasing = TRUE)

## evaluate precision recall curve
pr.curve(actual, response, indices = indices)

Usage

## Generic S3 method
## for Precision Recall Curve
pr.curve(...)

## Generic S3 method
## for weighted Precision Recall Curve
weighted.pr.curve(...)
## Generic S3 method
## for Precision Recall Curve
pr.curve(...)

## Generic S3 method
## for weighted Precision Recall Curve
weighted.pr.curve(...)

Arguments

...

Arguments passed on to pr.curve.factor, weighted.pr.curve.factor

actual: A vector length $n$ , and $k$ levels. Can be of integer or factor.
response: A $n \times k$ <double>-matrix of predicted probabilities. The $i$ -th row should sum to 1 (i.e., a valid probability distribution over the $k$ classes). The first column corresponds to the first factor level in actual, the second column to the second factor level, and so on.
indices: An optional $n \times k$ matrix of <integer> values of sorted response probability indices.
thresholds: An optional <double> vector of length $n$ (default: NULL).
w: A <double> vector of sample weights.

Value

A data.frame on the following form,

threshold

<numeric> Thresholds used to determine recall() and precision()

level

<character> The level of the actual <factor>

label

<character> The levels of the actual <factor>

recall

<numeric> The recall

precision

<numeric> The precision

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)

## Visualize precision recall curve

plot(
    SLmetrics::pr.curve(
     actual   = actual_classes, 
     response = probability_matrix
 )
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)

## Visualize precision recall curve

plot(
    SLmetrics::pr.curve(
     actual   = actual_classes, 
     response = probability_matrix
 )
)

Precision Recall Curve

Description

Defensive measures

To guard against this, wrap pr.curve() in a "safe" validator that checks for NA values and matching length, for example:

safe_pr.curve <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  pr.curve(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Area under the curve

Use auc.pr.curve for calculating the area under the curve directly.

Efficient multi-metric evaluation

## presort response
## probabilities
indices <- preorder(response, decreasing = TRUE)

## evaluate precision recall curve
pr.curve(actual, response, indices = indices)

Usage

## S3 method for class 'factor'
pr.curve(actual, response, thresholds = NULL, indices = NULL, ...)
## S3 method for class 'factor'
pr.curve(actual, response, thresholds = NULL, indices = NULL, ...)

Arguments

actual

A vector length $n$ , and $k$ levels. Can be of integer or factor.

response

thresholds

An optional <double> vector of length $n$ (default: NULL).

indices

An optional $n \times k$ matrix of <integer> values of sorted response probability indices.

...

Arguments passed into other methods.

Value

A data.frame on the following form,

threshold

<numeric> Thresholds used to determine recall() and precision()

level

<character> The level of the actual <factor>

label

<character> The levels of the actual <factor>

recall

<numeric> The recall

precision

<numeric> The precision

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)



## Visualize

plot(
    SLmetrics::pr.curve(
     actual   = actual_classes, 
     response = probability_matrix
 )
)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)



## Visualize

plot(
    SLmetrics::pr.curve(
     actual   = actual_classes, 
     response = probability_matrix
 )
)

Precision

Description

A generic S3 function to compute the precision score for a classification model. This function dispatches to S3 methods in precision() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because precision() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap precision() in a "safe" validator that checks for NA values and matching length, for example:

safe_precision <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  precision(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate precision
## via S3 dispatching
precision(confusion_matrix)

## additional performance metrics
## below

The precision.factor() method calls cmatrix() internally, so explicitly invoking precision.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## Generic S3 method
## for Precision
precision(...)

## Generic S3 method
## for weighted Precision
weighted.precision(...)
## Generic S3 method
## for Precision
precision(...)

## Generic S3 method
## for weighted Precision
weighted.precision(...)

Arguments

...

Arguments passed on to precision.factor, weighted.precision.factor, precision.cmatrix

actual,predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

w

A <double> vector of sample weights.

x

A confusion matrix created cmatrix().

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The precision has other names depending on research field:

Positive Predictive Value, ppv()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::precision(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::precision(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Precision

Description

Defensive measures

To guard against this, wrap precision() in a "safe" validator that checks for NA values and matching length, for example:

safe_precision <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  precision(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate precision
## via S3 dispatching
precision(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
precision(x, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'cmatrix'
precision(x, estimator = 0L, na.rm = TRUE, ...)

Arguments

x

A confusion matrix created cmatrix().

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The precision has other names depending on research field:

Positive Predictive Value, ppv()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::precision(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::precision(confusion_matrix)

Precision

Description

Defensive measures

To guard against this, wrap precision() in a "safe" validator that checks for NA values and matching length, for example:

safe_precision <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  precision(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate precision
## via S3 dispatching
precision(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
precision(actual, predicted, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'factor'
precision(actual, predicted, estimator = 0L, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The precision has other names depending on research field:

Positive Predictive Value, ppv()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::precision(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::precision(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Preorder Matrices

Description

A generic S3 function for somehting long. This function dispatches to S3 methods in preorder() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because preorder() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap preorder() in a “safe” validator that checks for NA values and matching length, for example:

safe_preorder <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  preorder(x, y, ...)
}

Usage

## Generic S3 method
## for Preorder Matrices
preorder(...)
## Generic S3 method
## for Preorder Matrices
preorder(...)

Arguments

...

Arguments passed on to preorder.matrix

x: A <matrix> to be sorted
decreasing: A <logical>

Value

A container of sorted indices

Preorder Matrices

Description

Defensive measures

To guard against this, wrap preorder() in a “safe” validator that checks for NA values and matching length, for example:

safe_preorder <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  preorder(x, y, ...)
}

Usage

## S3 method for class 'matrix'
preorder(x, decreasing = FALSE, ...)
## S3 method for class 'matrix'
preorder(x, decreasing = FALSE, ...)

Arguments

x

A <matrix> to be sorted

decreasing

A <logical>

...

Arguments passed into other methods

Value

A <matrix> of same dimensions as the input <matrix>

Presort Matrices

Description

A generic S3 function for somehting long. This function dispatches to S3 methods in presort() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because presort() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap presort() in a “safe” validator that checks for NA values and matching length, for example:

safe_presort <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  presort(x, y, ...)
}

Usage

## Generic S3 method
## for Presort Matrices
presort(...)
## Generic S3 method
## for Presort Matrices
presort(...)

Arguments

...

Arguments passed on to presort.matrix

x: A <matrix> to be sorted
decreasing: A <logical>

Value

A sorted container

Presort Matrices

Description

Defensive measures

To guard against this, wrap presort() in a “safe” validator that checks for NA values and matching length, for example:

safe_presort <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  presort(x, y, ...)
}

Usage

## S3 method for class 'matrix'
presort(x, decreasing = FALSE, ...)
## S3 method for class 'matrix'
presort(x, decreasing = FALSE, ...)

Arguments

x

A <matrix> to be sorted

decreasing

A <logical>

...

Arguments passed into other methods

Value

A <matrix> of same dimensions as the input <matrix>

Relative Absolute Error

Description

A generic S3 function to compute the relative absolute error score for a regression model. This function dispatches to S3 methods in rae() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because rae() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap rae() in a "safe" validator that checks for NA values and matching length, for example:

safe_rae <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rae(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Relative Absolute Error
rae(...)

## Generic S3 method
## for weighted Relative Absolute Error
weighted.rae(...)
## Generic S3 method
## for Relative Absolute Error
rae(...)

## Generic S3 method
## for weighted Relative Absolute Error
weighted.rae(...)

Arguments

...

Arguments passed on to rae.numeric, weighted.rae.numeric

actual,predicted: A pair of <double> vectors of length $n$ .
w: A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rae(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rae(
   actual    = actual_values, 
   predicted = predicted_values
)

Relative Absolute Error

Description

Defensive measures

To guard against this, wrap rae() in a "safe" validator that checks for NA values and matching length, for example:

safe_rae <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rae(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
rae(actual, predicted, ...)
## S3 method for class 'numeric'
rae(actual, predicted, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rae(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rae(
   actual    = actual_values, 
   predicted = predicted_values
)

Recall

Description

A generic S3 function to compute the recall score for a classification model. This function dispatches to S3 methods in recall() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because recall() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap recall() in a "safe" validator that checks for NA values and matching length, for example:

safe_recall <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  recall(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate recall
## via S3 dispatching
recall(confusion_matrix)

## additional performance metrics
## below

The recall.factor() method calls cmatrix() internally, so explicitly invoking recall.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## Generic S3 method
## for Recall
recall(...)

## Generic S3 method
## for weighted Recall
weighted.recall(...)
## Generic S3 method
## for Recall
recall(...)

## Generic S3 method
## for weighted Recall
weighted.recall(...)

Arguments

...

Arguments passed on to recall.factor, weighted.recall.factor, recall.cmatrix

actual,predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

w

A <double> vector of sample weights.

x

A confusion matrix created cmatrix().

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The Recall has other names depending on research field:

Sensitivity, sensitivity()
True Positive Rate, tpr()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::recall(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::recall(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Recall

Description

Defensive measures

To guard against this, wrap recall() in a "safe" validator that checks for NA values and matching length, for example:

safe_recall <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  recall(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate recall
## via S3 dispatching
recall(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
recall(x, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'cmatrix'
recall(x, estimator = 0L, na.rm = TRUE, ...)

Arguments

x

A confusion matrix created cmatrix().

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The Recall has other names depending on research field:

Sensitivity, sensitivity()
True Positive Rate, tpr()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::recall(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::recall(confusion_matrix)

Recall

Description

Defensive measures

To guard against this, wrap recall() in a "safe" validator that checks for NA values and matching length, for example:

safe_recall <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  recall(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate recall
## via S3 dispatching
recall(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
recall(actual, predicted, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'factor'
recall(actual, predicted, estimator = 0L, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The Recall has other names depending on research field:

Sensitivity, sensitivity()
True Positive Rate, tpr()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::recall(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::recall(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Relative Entropy

Description

A generic S3 function to compute the relative entropy score for a classification model. This function dispatches to S3 methods in relative.entropy() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because relative.entropy() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap relative.entropy() in a "safe" validator that checks for NA values and matching length, for example:

safe_relative.entropy <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  relative.entropy(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Relative Entropy
relative.entropy(...)
## Generic S3 method
## for Relative Entropy
relative.entropy(...)

Arguments

...

Arguments passed on to relative.entropy.matrix

pk,qk: A pair of <double> matrices of length $n$ of emprical probabilities $p$ and estimated probabilities $q$ .
dim: An <integer> value of length 1 (Default: 0). Defines the dimension along which to calculate the entropy (0: total, 1: row-wise, 2: column-wise).
normalize: A <logical>-value (default: TRUE). If TRUE, the mean cross-entropy across all observations is returned; otherwise, the sum of cross-entropies is returned.

Value

A <double> value or vector:

A single <double> value (length 1) if dim == 0.
A <double> vector with length equal to the length of columns if dim == 1.
A <double> vector with length equal to the length of rows if dim == 2.

References

MacKay, David JC. Information theory, inference and learning algorithms. Cambridge university press, 2003.

Kramer, Oliver, and Oliver Kramer. "Scikit-learn." Machine learning for evolution strategies (2016): 45-53.

Virtanen, Pauli, et al. "SciPy 1.0: f'undamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## generate valid probability
## distributions
rand.sum <- function(n) {
   x <- sort(runif( n-1 ))
   c(x,1) - c(0, x)
}

## empirical and
## predicted probabilites
set.seed(1903)
pk <- t(replicate(200,rand.sum(5)))
qk <- t(replicate(200,rand.sum(5)))

## entropy
relative.entropy(
 pk = pk,
 qk = qk
)



## generate valid probability
## distributions
rand.sum <- function(n) {
   x <- sort(runif( n-1 ))
   c(x,1) - c(0, x)
}

## empirical and
## predicted probabilites
set.seed(1903)
pk <- t(replicate(200,rand.sum(5)))
qk <- t(replicate(200,rand.sum(5)))

## entropy
relative.entropy(
 pk = pk,
 qk = qk
)

Relative Entropy

Description

Defensive measures

To guard against this, wrap relative.entropy() in a "safe" validator that checks for NA values and matching length, for example:

safe_relative.entropy <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  relative.entropy(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'matrix'
relative.entropy(pk, qk, dim = 0L, normalize = FALSE, ...)
## S3 method for class 'matrix'
relative.entropy(pk, qk, dim = 0L, normalize = FALSE, ...)

Arguments

pk, qk

A pair of <double> matrices of length $n$ of emprical probabilities $p$ and estimated probabilities $q$ .

dim

An <integer> value of length 1 (Default: 0). Defines the dimension along which to calculate the entropy (0: total, 1: row-wise, 2: column-wise).

normalize

A <logical>-value (default: TRUE). If TRUE, the mean cross-entropy across all observations is returned; otherwise, the sum of cross-entropies is returned.

...

Arguments passed into other methods.

Value

A <double> value or vector:

A single <double> value (length 1) if dim == 0.
A <double> vector with length equal to the length of columns if dim == 1.
A <double> vector with length equal to the length of rows if dim == 2.

References

MacKay, David JC. Information theory, inference and learning algorithms. Cambridge university press, 2003.

Kramer, Oliver, and Oliver Kramer. "Scikit-learn." Machine learning for evolution strategies (2016): 45-53.

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## generate valid probability
## distributions
rand.sum <- function(n) {
   x <- sort(runif( n-1 ))
   c(x,1) - c(0, x)
}

## empirical and
## predicted probabilites
set.seed(1903)
pk <- t(replicate(200,rand.sum(5)))
qk <- t(replicate(200,rand.sum(5)))

## entropy
relative.entropy(
 pk = pk,
 qk = qk
)



## generate valid probability
## distributions
rand.sum <- function(n) {
   x <- sort(runif( n-1 ))
   c(x,1) - c(0, x)
}

## empirical and
## predicted probabilites
set.seed(1903)
pk <- t(replicate(200,rand.sum(5)))
qk <- t(replicate(200,rand.sum(5)))

## entropy
relative.entropy(
 pk = pk,
 qk = qk
)

Root Mean Squared Error

Description

A generic S3 function to compute the root mean squared error score for a regression model. This function dispatches to S3 methods in rmse() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because rmse() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap rmse() in a "safe" validator that checks for NA values and matching length, for example:

safe_rmse <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rmse(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Root Mean Squared Error
rmse(...)

## Generic S3 method
## for weighted Root Mean Squared Error
weighted.rmse(...)
## Generic S3 method
## for Root Mean Squared Error
rmse(...)

## Generic S3 method
## for weighted Root Mean Squared Error
weighted.rmse(...)

Arguments

...

Arguments passed on to rmse.numeric, weighted.rmse.numeric

actual,predicted: A pair of <double> vectors of length $n$ .
w: A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rmse(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rmse(
   actual    = actual_values, 
   predicted = predicted_values
)

Root Mean Squared Error

Description

Defensive measures

To guard against this, wrap rmse() in a "safe" validator that checks for NA values and matching length, for example:

safe_rmse <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rmse(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
rmse(actual, predicted, ...)
## S3 method for class 'numeric'
rmse(actual, predicted, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rmse(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rmse(
   actual    = actual_values, 
   predicted = predicted_values
)

Root Mean Squared Logarithmic Error

Description

A generic S3 function to compute the root mean squared logarithmic error score for a regression model. This function dispatches to S3 methods in rmsle() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because rmsle() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap rmsle() in a "safe" validator that checks for NA values and matching length, for example:

safe_rmsle <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rmsle(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Root Mean Squared Logarithmic Error
rmsle(...)

## Generic S3 method
## for weighted Root Mean Squared Logarithmic Error
weighted.rmsle(...)
## Generic S3 method
## for Root Mean Squared Logarithmic Error
rmsle(...)

## Generic S3 method
## for weighted Root Mean Squared Logarithmic Error
weighted.rmsle(...)

Arguments

...

Arguments passed on to rmsle.numeric, weighted.rmsle.numeric

actual,predicted: A pair of <double> vectors of length $n$ .
w: A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rmsle(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rmsle(
   actual    = actual_values, 
   predicted = predicted_values
)

Root Mean Squared Logarithmic Error

Description

Defensive measures

To guard against this, wrap rmsle() in a "safe" validator that checks for NA values and matching length, for example:

safe_rmsle <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rmsle(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
rmsle(actual, predicted, ...)
## S3 method for class 'numeric'
rmsle(actual, predicted, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rmsle(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rmsle(
   actual    = actual_values, 
   predicted = predicted_values
)

Reciever Operator Characteristics

Description

A generic S3 function to compute the reciever operator characteristics score for a classification model. This function dispatches to S3 methods in roc.curve() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because roc.curve() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap roc.curve() in a "safe" validator that checks for NA values and matching length, for example:

safe_roc.curve <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  roc.curve(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Area under the curve

Use auc.roc.curve for calculating the area under the curve directly.

Efficient multi-metric evaluation

## presort response
## probabilities
indices <- preorder(response, decreasing = TRUE)

## evaluate reciever operator characteristics
roc.curve(actual, response, indices = indices)

Usage

## Generic S3 method
## for Reciever Operator Characteristics
roc.curve(...)

## Generic S3 method
## for weighted Reciever Operator Characteristics
weighted.roc.curve(...)
## Generic S3 method
## for Reciever Operator Characteristics
roc.curve(...)

## Generic S3 method
## for weighted Reciever Operator Characteristics
weighted.roc.curve(...)

Arguments

...

Arguments passed on to roc.curve.factor, weighted.roc.curve.factor

actual: A vector length $n$ , and $k$ levels. Can be of integer or factor.
response: A $n \times k$ <double>-matrix of predicted probabilities. The $i$ -th row should sum to 1 (i.e., a valid probability distribution over the $k$ classes). The first column corresponds to the first factor level in actual, the second column to the second factor level, and so on.
indices: An optional $n \times k$ matrix of <integer> values of sorted response probability indices.
thresholds: An optional <double> vector of length $n$ (default: NULL).
w: A <double> vector of sample weights.

Value

A data.frame on the following form,

threshold

<numeric> Thresholds used to determine tpr() and fpr()

level

<character> The level of the actual <factor>

label

<character> The levels of the actual <factor>

fpr

<numeric> The false positive rate

tpr

<numeric> The true positve rate

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)

## Visualize reciever operator characteristics

plot(
    SLmetrics::roc.curve(
     actual   = actual_classes, 
     response = probability_matrix
 )
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)

## Visualize reciever operator characteristics

plot(
    SLmetrics::roc.curve(
     actual   = actual_classes, 
     response = probability_matrix
 )
)

Reciever Operator Characteristics

Description

Defensive measures

To guard against this, wrap roc.curve() in a "safe" validator that checks for NA values and matching length, for example:

safe_roc.curve <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  roc.curve(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Area under the curve

Use auc.roc.curve for calculating the area under the curve directly.

Efficient multi-metric evaluation

## presort response
## probabilities
indices <- preorder(response, decreasing = TRUE)

## evaluate reciever operator characteristics
roc.curve(actual, response, indices = indices)

Usage

## S3 method for class 'factor'
roc.curve(actual, response, thresholds = NULL, indices = NULL, ...)
## S3 method for class 'factor'
roc.curve(actual, response, thresholds = NULL, indices = NULL, ...)

Arguments

actual

A vector length $n$ , and $k$ levels. Can be of integer or factor.

response

thresholds

An optional <double> vector of length $n$ (default: NULL).

indices

An optional $n \times k$ matrix of <integer> values of sorted response probability indices.

...

Arguments passed into other methods.

Value

A data.frame on the following form,

threshold

<numeric> Thresholds used to determine tpr() and fpr()

level

<character> The level of the actual <factor>

label

<character> The levels of the actual <factor>

fpr

<numeric> The false positive rate

tpr

<numeric> The true positve rate

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)



## Visualize

plot(
    SLmetrics::roc.curve(
     actual   = actual_classes, 
     response = probability_matrix
 )
)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)



## Visualize

plot(
    SLmetrics::roc.curve(
     actual   = actual_classes, 
     response = probability_matrix
 )
)

Relative Root Mean Squared Error

Description

A generic S3 function to compute the relative root mean squared error score for a regression model. This function dispatches to S3 methods in rrmse() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because rrmse() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap rrmse() in a "safe" validator that checks for NA values and matching length, for example:

safe_rrmse <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rrmse(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Relative Root Mean Squared Error
rrmse(...)

## Generic S3 method
## for weighted Concordance Correlation Coefficient
weighted.rrmse(...)
## Generic S3 method
## for Relative Root Mean Squared Error
rrmse(...)

## Generic S3 method
## for weighted Concordance Correlation Coefficient
weighted.rrmse(...)

Arguments

...

Arguments passed on to rrmse.numeric, weighted.rrmse.numeric

actual,predicted: A pair of <double> vectors of length $n$ .
normalization: A <double>-value of length $1$ (default: $1$ ). $0$ : mean-normalization, $1$ : range-normalization, $2$ : IQR-normalization.
w: A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rrmse(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rrmse(
   actual    = actual_values, 
   predicted = predicted_values
)

Relative Root Mean Squared Error

Description

Defensive measures

To guard against this, wrap rrmse() in a "safe" validator that checks for NA values and matching length, for example:

safe_rrmse <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rrmse(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
rrmse(actual, predicted, normalization = 1L, ...)
## S3 method for class 'numeric'
rrmse(actual, predicted, normalization = 1L, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

normalization

A <double>-value of length $1$ (default: $1$ ). $0$ : mean-normalization, $1$ : range-normalization, $2$ : IQR-normalization.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rrmse(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rrmse(
   actual    = actual_values, 
   predicted = predicted_values
)

Root Relative Squared Error

Description

A generic S3 function to compute the root relative squared error score for a regression model. This function dispatches to S3 methods in rrse() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because rrse() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap rrse() in a "safe" validator that checks for NA values and matching length, for example:

safe_rrse <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rrse(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Root Relative Squared Error
rrse(...)

## Generic S3 method
## for weighted Root Relative Squared Error
weighted.rrse(...)
## Generic S3 method
## for Root Relative Squared Error
rrse(...)

## Generic S3 method
## for weighted Root Relative Squared Error
weighted.rrse(...)

Arguments

...

Arguments passed on to rrse.numeric, weighted.rrse.numeric

actual,predicted: A pair of <double> vectors of length $n$ .
w: A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rrse(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rrse(
   actual    = actual_values, 
   predicted = predicted_values
)

Root Relative Squared Error

Description

Defensive measures

To guard against this, wrap rrse() in a "safe" validator that checks for NA values and matching length, for example:

safe_rrse <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rrse(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
rrse(actual, predicted, ...)
## S3 method for class 'numeric'
rrse(actual, predicted, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rrse(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rrse(
   actual    = actual_values, 
   predicted = predicted_values
)

$r^2$

Description

A generic S3 function to compute the $r^2$ score for a regression model. This function dispatches to S3 methods in rsq() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because rsq() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap rsq() in a "safe" validator that checks for NA values and matching length, for example:

safe_rsq <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rsq(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for \eqn{r^2}
rsq(...)

## Generic S3 method
## for weighted \eqn{r^2}
weighted.rsq(...)
## Generic S3 method
## for \eqn{r^2}
rsq(...)

## Generic S3 method
## for weighted \eqn{r^2}
weighted.rsq(...)

Arguments

...

Arguments passed on to rsq.numeric, weighted.rsq.numeric

actual,predicted: A pair of <double> vectors of length $n$ .
k: A <double>-vector of length 1 (default: 0). For adjusted $R^2$ set $k = \kappa - 1$ , where $\kappa$ is the number of parameters.
w: A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rsq(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rsq(
   actual    = actual_values, 
   predicted = predicted_values
)

$r^2$

Description

Defensive measures

To guard against this, wrap rsq() in a "safe" validator that checks for NA values and matching length, for example:

safe_rsq <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rsq(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
rsq(actual, predicted, k = 0, ...)
## S3 method for class 'numeric'
rsq(actual, predicted, k = 0, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

k

A <double>-vector of length 1 (default: 0). For adjusted $R^2$ set $k = \kappa - 1$ , where $\kappa$ is the number of parameters.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rsq(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::rsq(
   actual    = actual_values, 
   predicted = predicted_values
)

Shannon Entropy

Description

A generic S3 function to compute the shannon entropy score for a classification model. This function dispatches to S3 methods in shannon.entropy() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because shannon.entropy() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap shannon.entropy() in a "safe" validator that checks for NA values and matching length, for example:

safe_shannon.entropy <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  shannon.entropy(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Shannon Entropy
shannon.entropy(...)
## Generic S3 method
## for Shannon Entropy
shannon.entropy(...)

Arguments

...

Arguments passed on to shannon.entropy.matrix

pk: A $n \times k$ <double>-matrix of observed probabilities. The $i$ -th row should sum to 1 (i.e., a valid probability distribution over the $k$ classes). The first column corresponds to the first factor level in actual, the second column to the second factor level, and so on.
dim: An <integer> value of length 1 (Default: 0). Defines the dimension along which to calculate the entropy (0: total, 1: row-wise, 2: column-wise).
normalize: A <logical>-value (default: TRUE). If TRUE, the mean cross-entropy across all observations is returned; otherwise, the sum of cross-entropies is returned.

Value

A <double> value or vector:

A single <double> value (length 1) if dim == 0.
A <double> vector with length equal to the length of columns if dim == 1.
A <double> vector with length equal to the length of rows if dim == 2.

References

MacKay, David JC. Information theory, inference and learning algorithms. Cambridge university press, 2003.

Kramer, Oliver, and Oliver Kramer. "Scikit-learn." Machine learning for evolution strategies (2016): 45-53.

Virtanen, Pauli, et al. "SciPy 1.0: f'undamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## generate valid probability
## distributions
rand.sum <- function(n) {
   x <- sort(runif( n-1 ))
   c(x,1) - c(0, x)
}



## empirical and
## predicted probabilites
set.seed(1903)
pk <- t(replicate(200,rand.sum(5)))

## entropy
SLmetrics::shannon.entropy(
 pk = pk
)




## generate valid probability
## distributions
rand.sum <- function(n) {
   x <- sort(runif( n-1 ))
   c(x,1) - c(0, x)
}



## empirical and
## predicted probabilites
set.seed(1903)
pk <- t(replicate(200,rand.sum(5)))

## entropy
SLmetrics::shannon.entropy(
 pk = pk
)

Shannon Entropy

Description

Defensive measures

To guard against this, wrap shannon.entropy() in a "safe" validator that checks for NA values and matching length, for example:

safe_shannon.entropy <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  shannon.entropy(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'matrix'
shannon.entropy(pk, dim = 0L, normalize = FALSE, ...)
## S3 method for class 'matrix'
shannon.entropy(pk, dim = 0L, normalize = FALSE, ...)

Arguments

pk

A $n \times k$ <double>-matrix of observed probabilities. The $i$ -th row should sum to 1 (i.e., a valid probability distribution over the $k$ classes). The first column corresponds to the first factor level in actual, the second column to the second factor level, and so on.

dim

An <integer> value of length 1 (Default: 0). Defines the dimension along which to calculate the entropy (0: total, 1: row-wise, 2: column-wise).

normalize

A <logical>-value (default: TRUE). If TRUE, the mean cross-entropy across all observations is returned; otherwise, the sum of cross-entropies is returned.

...

Arguments passed into other methods.

Value

A <double> value or vector:

A single <double> value (length 1) if dim == 0.
A <double> vector with length equal to the length of columns if dim == 1.
A <double> vector with length equal to the length of rows if dim == 2.

References

MacKay, David JC. Information theory, inference and learning algorithms. Cambridge university press, 2003.

Kramer, Oliver, and Oliver Kramer. "Scikit-learn." Machine learning for evolution strategies (2016): 45-53.

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## generate valid probability
## distributions
rand.sum <- function(n) {
   x <- sort(runif( n-1 ))
   c(x,1) - c(0, x)
}



## empirical and
## predicted probabilites
set.seed(1903)
pk <- t(replicate(200,rand.sum(5)))

## entropy
SLmetrics::shannon.entropy(
 pk = pk
)




## generate valid probability
## distributions
rand.sum <- function(n) {
   x <- sort(runif( n-1 ))
   c(x,1) - c(0, x)
}



## empirical and
## predicted probabilites
set.seed(1903)
pk <- t(replicate(200,rand.sum(5)))

## entropy
SLmetrics::shannon.entropy(
 pk = pk
)

Symmetric Mean Absolutte Percentage Error

Description

A generic S3 function to compute the symmetric mean absolutte percentage error score for a regression model. This function dispatches to S3 methods in smape() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because smape() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap smape() in a "safe" validator that checks for NA values and matching length, for example:

safe_smape <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  smape(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## Generic S3 method
## for Symmetric Mean Absolutte Percentage Error
smape(...)

## Generic S3 method
## for weighted Symmetric Mean Absolutte Percentage Error
weighted.smape(...)
## Generic S3 method
## for Symmetric Mean Absolutte Percentage Error
smape(...)

## Generic S3 method
## for weighted Symmetric Mean Absolutte Percentage Error
weighted.smape(...)

Arguments

...

Arguments passed on to smape.numeric, weighted.smape.numeric

actual,predicted: A pair of <double> vectors of length $n$ .
w: A <double> vector of sample weights.

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::smape(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::smape(
   actual    = actual_values, 
   predicted = predicted_values
)

Symmetric Mean Absolutte Percentage Error

Description

Defensive measures

To guard against this, wrap smape() in a "safe" validator that checks for NA values and matching length, for example:

safe_smape <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  smape(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
smape(actual, predicted, ...)
## S3 method for class 'numeric'
smape(actual, predicted, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::smape(
   actual    = actual_values, 
   predicted = predicted_values
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::smape(
   actual    = actual_values, 
   predicted = predicted_values
)

Specificity

Description

A generic S3 function to compute the specificity score for a classification model. This function dispatches to S3 methods in specificity() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because specificity() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap specificity() in a "safe" validator that checks for NA values and matching length, for example:

safe_specificity <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  specificity(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate specificity
## via S3 dispatching
specificity(confusion_matrix)

## additional performance metrics
## below

The specificity.factor() method calls cmatrix() internally, so explicitly invoking specificity.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## Generic S3 method
## for Specificity
specificity(...)

## Generic S3 method
## for weighted Specificity
weighted.specificity(...)
## Generic S3 method
## for Specificity
specificity(...)

## Generic S3 method
## for weighted Specificity
weighted.specificity(...)

Arguments

...

Arguments passed on to specificity.factor, weighted.specificity.factor, specificity.cmatrix

actual,predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

w

A <double> vector of sample weights.

x

A confusion matrix created cmatrix().

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The specificity has other names depending on research field:

True Negative Rate, tnr()
Selectivity, selectivity()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::specificity(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::specificity(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Specificity

Description

Defensive measures

To guard against this, wrap specificity() in a "safe" validator that checks for NA values and matching length, for example:

safe_specificity <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  specificity(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate specificity
## via S3 dispatching
specificity(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
specificity(x, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'cmatrix'
specificity(x, estimator = 0L, na.rm = TRUE, ...)

Arguments

x

A confusion matrix created cmatrix().

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The specificity has other names depending on research field:

True Negative Rate, tnr()
Selectivity, selectivity()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::specificity(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::specificity(confusion_matrix)

Specificity

Description

Defensive measures

To guard against this, wrap specificity() in a "safe" validator that checks for NA values and matching length, for example:

safe_specificity <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  specificity(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate specificity
## via S3 dispatching
specificity(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
specificity(actual, predicted, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'factor'
specificity(actual, predicted, estimator = 0L, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The specificity has other names depending on research field:

True Negative Rate, tnr()
Selectivity, selectivity()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::specificity(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::specificity(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Accuracy

Description

Defensive measures

To guard against this, wrap accuracy() in a "safe" validator that checks for NA values and matching length, for example:

safe_accuracy <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  accuracy(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate accuracy
## via S3 dispatching
accuracy(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
weighted.accuracy(actual, predicted, w, ...)
## S3 method for class 'factor'
weighted.accuracy(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.accuracy(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.accuracy(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

Area under the Precision Recall Curve

Description

Defensive measures

To guard against this, wrap auc.pr.curve() in a "safe" validator that checks for NA values and matching length, for example:

safe_auc.pr.curve <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  auc.pr.curve(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Visualizing area under the precision recall curve

Use pr.curve() to construct the data.frame and use plot to visualize the area under the curve.

Efficient multi-metric evaluation

## presort response
## probabilities
indices <- preorder(response, decreasing = TRUE)

## evaluate area under the precision recall curve
auc.pr.curve(actual, response, indices = indices)

Usage

## S3 method for class 'factor'
weighted.auc.pr.curve(
  actual,
  response,
  w,
  estimator = 0L,
  method = 0L,
  indices = NULL,
  ...
)
## S3 method for class 'factor'
weighted.auc.pr.curve(
  actual,
  response,
  w,
  estimator = 0L,
  method = 0L,
  indices = NULL,
  ...
)

Arguments

actual

A vector length $n$ , and $k$ levels. Can be of integer or factor.

response

w

A <double> vector of sample weights.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

method

indices

An optional $n \times k$ matrix of <integer> values of sorted response probability indices.

...

Arguments passed into other methods.

Value

If estimator is given as

0: a named <double>-vector of length k
1: a <double> value (Micro averaged metric)
2: a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)


sample_weights <- runif(1e2)

## Evaluate performance

SLmetrics::weighted.auc.pr.curve(
    actual   = actual_classes, 
    response = probability_matrix,
    w        = sample_weights
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)


sample_weights <- runif(1e2)

## Evaluate performance

SLmetrics::weighted.auc.pr.curve(
    actual   = actual_classes, 
    response = probability_matrix,
    w        = sample_weights
)

Area under the Receiver Operator Characteristics Curve

Description

Defensive measures

To guard against this, wrap auc.roc.curve() in a "safe" validator that checks for NA values and matching length, for example:

safe_auc.roc.curve <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  auc.roc.curve(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Visualizing area under the receiver operator characteristics curve

Use roc.curve() to construct the data.frame and use plot to visualize the area under the curve.

Efficient multi-metric evaluation

## presort response
## probabilities
indices <- preorder(response, decreasing = TRUE)

## evaluate area under the receiver operator characteristics curve
auc.roc.curve(actual, response, indices = indices)

Usage

## S3 method for class 'factor'
weighted.auc.roc.curve(
  actual,
  response,
  w,
  estimator = 0L,
  method = 0L,
  indices = NULL,
  ...
)
## S3 method for class 'factor'
weighted.auc.roc.curve(
  actual,
  response,
  w,
  estimator = 0L,
  method = 0L,
  indices = NULL,
  ...
)

Arguments

actual

A vector length $n$ , and $k$ levels. Can be of integer or factor.

response

w

A <double> vector of sample weights.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

method

indices

An optional $n \times k$ matrix of <integer> values of sorted response probability indices.

...

Arguments passed into other methods.

Value

If estimator is given as

0: a named <double>-vector of length k
1: a <double> value (Micro averaged metric)
2: a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)


sample_weights <- runif(1e2)

## Evaluate performance

SLmetrics::weighted.auc.roc.curve(
    actual   = actual_classes, 
    response = probability_matrix,
    w        = sample_weights
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)


sample_weights <- runif(1e2)

## Evaluate performance

SLmetrics::weighted.auc.roc.curve(
    actual   = actual_classes, 
    response = probability_matrix,
    w        = sample_weights
)

Balanced Accuracy

Description

Defensive measures

To guard against this, wrap baccuracy() in a "safe" validator that checks for NA values and matching length, for example:

safe_baccuracy <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  baccuracy(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate balanced accuracy
## via S3 dispatching
baccuracy(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
weighted.baccuracy(actual, predicted, w, adjust = FALSE, na.rm = TRUE, ...)
## S3 method for class 'factor'
weighted.baccuracy(actual, predicted, w, adjust = FALSE, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

adjust

A <logical> value (default: FALSE). If TRUE the metric is adjusted for random chance $\frac{1}{k}$ .

na.rm

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.baccuracy(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.baccuracy(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

Brier Score

Description

Defensive measures

To guard against this, wrap brier.score() in a "safe" validator that checks for NA values and matching length, for example:

safe_brier.score <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  brier.score(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'matrix'
weighted.brier.score(ok, qk, w, ...)
## S3 method for class 'matrix'
weighted.brier.score(ok, qk, w, ...)

Arguments

ok

A <double> indicator matrix with $n$ samples and $k$ classes.

qk

w

A <double> vector of sample weights.

...

Arguments passed into other methods.

Value

A <double>-value

References

Gneiting, Tilmann, and Adrian E. Raftery. "Strictly proper scoring rules, prediction, and estimation." Journal of the American statistical Association 102.477 (2007): 359-378.

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## seed
set.seed(1903)

## The general setup
## with 3 classes
n_obs     <- 10
n_classes <- 3

## Generate indicator matrix
## with observed outcome (ok) and 
## its predicted probability matrix (qk)
ok <- diag(n_classes)[ sample.int(n_classes, n_obs, TRUE), ]
qk <- matrix(runif(n_obs * n_classes), n_obs, n_classes)
qk <- qk / rowSums(qk)


## Generate sample
## weights
sample_weights <- runif(
   n = n_obs
)

## Evaluate performance
SLmetrics::weighted.brier.score(
   ok = ok, 
   qk = qk,
   w  = sample_weights
)

## seed
set.seed(1903)

## The general setup
## with 3 classes
n_obs     <- 10
n_classes <- 3

## Generate indicator matrix
## with observed outcome (ok) and 
## its predicted probability matrix (qk)
ok <- diag(n_classes)[ sample.int(n_classes, n_obs, TRUE), ]
qk <- matrix(runif(n_obs * n_classes), n_obs, n_classes)
qk <- qk / rowSums(qk)


## Generate sample
## weights
sample_weights <- runif(
   n = n_obs
)

## Evaluate performance
SLmetrics::weighted.brier.score(
   ok = ok, 
   qk = qk,
   w  = sample_weights
)

Concordance Correlation Coefficient

Description

Defensive measures

To guard against this, wrap ccc() in a "safe" validator that checks for NA values and matching length, for example:

safe_ccc <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  ccc(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.ccc(actual, predicted, w, correction = FALSE, ...)
## S3 method for class 'numeric'
weighted.ccc(actual, predicted, w, correction = FALSE, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

correction

A <logical> vector of length $1$ (default: FALSE). If TRUE the variance and covariance will be adjusted with $\frac{1-n}{n}$

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.ccc(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.ccc(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

Cohen's $\kappa$ -Statistic

Description

Defensive measures

To guard against this, wrap ckappa() in a "safe" validator that checks for NA values and matching length, for example:

safe_ckappa <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  ckappa(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate cohen's \eqn{\kappa}-statistic
## via S3 dispatching
ckappa(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
weighted.ckappa(actual, predicted, w, beta = 0, ...)
## S3 method for class 'factor'
weighted.ckappa(actual, predicted, w, beta = 0, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

beta

A <double> value of length 1 (default: 0). If $\beta \neq 0$ the off-diagonals of the confusion matrix are penalized with a factor of $(y_{+} - y_{i,-})^\beta$ .

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.ckappa(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.ckappa(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

Confusion Matrix

Description

Defensive measures

To guard against this, wrap cmatrix() in a "safe" validator that checks for NA values and matching length, for example:

safe_cmatrix <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  cmatrix(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

The workhorse

## Compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## Evaluate accuracy
## via S3 dispatching
accuracy(confusion_matrix)

## Evaluate recall
## via S3 dispatching
recall(confusion_matrix)

Usage

## S3 method for class 'factor'
weighted.cmatrix(actual, predicted, w, ...)
## S3 method for class 'factor'
weighted.cmatrix(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

...

Arguments passed into other methods.

Value

A named $k$ x $k$ <matrix>

Dimensions

There is no robust defensive measure against misspecifying the confusion matrix. If the arguments are passed correctly, the resulting confusion matrix is on the form:

	A (Predicted)	B (Predicted)
A (Actual)	Value	Value
B (Actual)	Value	Value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Compute confusion matrix
SLmetrics::weighted.cmatrix(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Compute confusion matrix
SLmetrics::weighted.cmatrix(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

Gamma Deviance

Description

Defensive measures

To guard against this, wrap deviance.gamma() in a "safe" validator that checks for NA values and matching length, for example:

safe_deviance.gamma <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  deviance.gamma(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.deviance.gamma(actual, predicted, w, ...)
## S3 method for class 'numeric'
weighted.deviance.gamma(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.deviance.gamma(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.deviance.gamma(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

Poisson Deviance

Description

Defensive measures

To guard against this, wrap deviance.poisson() in a "safe" validator that checks for NA values and matching length, for example:

safe_deviance.poisson <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  deviance.poisson(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.deviance.poisson(actual, predicted, w, ...)
## S3 method for class 'numeric'
weighted.deviance.poisson(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.deviance.poisson(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.deviance.poisson(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

Tweedie Deviance

Description

Defensive measures

To guard against this, wrap deviance.tweedie() in a "safe" validator that checks for NA values and matching length, for example:

safe_deviance.tweedie <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  deviance.tweedie(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.deviance.tweedie(actual, predicted, w, power = 2, ...)
## S3 method for class 'numeric'
weighted.deviance.tweedie(actual, predicted, w, power = 2, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

power

A <double> value, default = 2. Tweedie power parameter. Either power <= 0 or power >= 1.

The higher $power$ , the less weight is given to extreme deviations between actual and predicted values.

power < 0: Extreme stable distribution. Requires: predicted > 0.
power = 0: Normal distribution, output corresponds to mse(), actual and predicted can be any real numbers.
power = 1: Poisson distribution (deviance.poisson()). Requires: actual >= 0 and predicted > 0.
1 < power < 2: Compound Poisson distribution. Requires: actual >= 0 and predicted > 0.
power = 2: Gamma distribution (deviance.gamma()). Requires: actual > 0 and predicted > 0.
power = 3: Inverse Gaussian distribution. Requires: actual > 0 and predicted > 0.
otherwise: Positive stable distribution. Requires: actual > 0 and predicted > 0.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.deviance.tweedie(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.deviance.tweedie(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

Positive Likelihood Ratio

Description

Defensive measures

To guard against this, wrap plr() in a "safe" validator that checks for NA values and matching length, for example:

safe_plr <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  plr(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate positive likelihood ratio
## via S3 dispatching
plr(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
weighted.dor(actual, predicted, w, ...)
## S3 method for class 'factor'
weighted.dor(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.dor(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.dor(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

$f_{\beta}$

Description

Defensive measures

To guard against this, wrap fbeta() in a "safe" validator that checks for NA values and matching length, for example:

safe_fbeta <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fbeta(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate \eqn{f_{\beta}}
## via S3 dispatching
fbeta(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
weighted.fbeta(
  actual,
  predicted,
  w,
  beta = 1,
  estimator = 0L,
  na.rm = TRUE,
  ...
)
## S3 method for class 'factor'
weighted.fbeta(
  actual,
  predicted,
  w,
  beta = 1,
  estimator = 0L,
  na.rm = TRUE,
  ...
)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

beta

A <double> vector of length $1$ (default: $1$ ).

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.fbeta(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.fbeta(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

False Discovery Rate

Description

Defensive measures

To guard against this, wrap fdr() in a "safe" validator that checks for NA values and matching length, for example:

safe_fdr <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fdr(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate false discovery rate
## via S3 dispatching
fdr(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
weighted.fdr(actual, predicted, w, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'factor'
weighted.fdr(actual, predicted, w, estimator = 0L, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.fdr(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.fdr(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

False Omission Rate

Description

Defensive measures

To guard against this, wrap fer() in a "safe" validator that checks for NA values and matching length, for example:

safe_fer <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fer(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate false omission rate
## via S3 dispatching
fer(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
weighted.fer(actual, predicted, w, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'factor'
weighted.fer(actual, predicted, w, estimator = 0L, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.fer(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.fer(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

Fowlkes Mallows Index

Description

Defensive measures

To guard against this, wrap fmi() in a "safe" validator that checks for NA values and matching length, for example:

safe_fmi <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fmi(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate fowlkes mallows index
## via S3 dispatching
fmi(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
weighted.fmi(actual, predicted, w, ...)
## S3 method for class 'factor'
weighted.fmi(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.fmi(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.fmi(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

False Positive Rate

Description

Defensive measures

To guard against this, wrap fpr() in a "safe" validator that checks for NA values and matching length, for example:

safe_fpr <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fpr(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate false positive rate
## via S3 dispatching
fpr(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
weighted.fpr(actual, predicted, w, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'factor'
weighted.fpr(actual, predicted, w, estimator = 0L, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The false positive rate has other names depending on research field:

Fallout, fallout()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.fpr(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.fpr(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

Geometric Mean Squared Error

Description

Defensive measures

To guard against this, wrap gmse() in a "safe" validator that checks for NA values and matching length, for example:

safe_gmse <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  gmse(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.gmse(actual, predicted, w, ...)
## S3 method for class 'numeric'
weighted.gmse(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.gmse(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.gmse(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

Hamming Loss

Description

Defensive measures

To guard against this, wrap hammingloss() in a "safe" validator that checks for NA values and matching length, for example:

safe_hammingloss <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  hammingloss(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate hamming loss
## via S3 dispatching
hammingloss(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
weighted.hammingloss(actual, predicted, w, ...)
## S3 method for class 'factor'
weighted.hammingloss(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.hammingloss(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.hammingloss(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

Huber Loss

Description

Defensive measures

To guard against this, wrap huberloss() in a "safe" validator that checks for NA values and matching length, for example:

safe_huberloss <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  huberloss(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.huberloss(actual, predicted, w, delta = 1, ...)
## S3 method for class 'numeric'
weighted.huberloss(actual, predicted, w, delta = 1, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

delta

A <double>-vector of length $1$ (default: $1$ ). The threshold value for switch between functions (see calculation).

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.huberloss(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.huberloss(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

Jaccard Index

Description

Defensive measures

To guard against this, wrap jaccard() in a "safe" validator that checks for NA values and matching length, for example:

safe_jaccard <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  jaccard(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate jaccard index
## via S3 dispatching
jaccard(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
weighted.jaccard(actual, predicted, w, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'factor'
weighted.jaccard(actual, predicted, w, estimator = 0L, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The specificity has other names depending on research field:

Critical Success Index, csi()
Threat Score, tscore()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.jaccard(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.jaccard(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

Logarithmic Loss

Description

Defensive measures

To guard against this, wrap logloss() in a "safe" validator that checks for NA values and matching length, for example:

safe_logloss <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  logloss(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'factor'
weighted.logloss(actual, response, w, normalize = TRUE, ...)
## S3 method for class 'factor'
weighted.logloss(actual, response, w, normalize = TRUE, ...)

Arguments

actual

A vector length $n$ , and $k$ levels. Can be of integer or factor.

response

w

A <double> vector of sample weights.

normalize

A <logical>-value (default: TRUE). If TRUE, the mean cross-entropy across all observations is returned; otherwise, the sum of cross-entropies is returned.

...

Arguments passed into other methods.

Value

A <double>

References

MacKay, David JC. Information theory, inference and learning algorithms. Cambridge university press, 2003.

Kramer, Oliver, and Oliver Kramer. "Scikit-learn." Machine learning for evolution strategies (2016): 45-53.

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted response
## probabilities
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

response <- runif(n = 1e3)

## Generate sample
## weights
sample_weights <- runif(
  n = 1e3
)


## Evaluate performance
SLmetrics::weighted.logloss(
   actual    = actual_classes, 
   response  = cbind(
     response,
     1 - response
   ),
   w = sample_weights
)

## Generate observed
## frequencies 
actual_frequency <- sample(10L:100L, size = 1e3, replace = TRUE)

SLmetrics::weighted.logloss(
   actual    = actual_frequency, 
   response  = response,
   w         = sample_weights
)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted response
## probabilities
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

response <- runif(n = 1e3)

## Generate sample
## weights
sample_weights <- runif(
  n = 1e3
)


## Evaluate performance
SLmetrics::weighted.logloss(
   actual    = actual_classes, 
   response  = cbind(
     response,
     1 - response
   ),
   w = sample_weights
)

## Generate observed
## frequencies 
actual_frequency <- sample(10L:100L, size = 1e3, replace = TRUE)

SLmetrics::weighted.logloss(
   actual    = actual_frequency, 
   response  = response,
   w         = sample_weights
)

Logarithmic Loss

Description

Defensive measures

To guard against this, wrap logloss() in a "safe" validator that checks for NA values and matching length, for example:

safe_logloss <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  logloss(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'integer'
weighted.logloss(actual, response, w, normalize = TRUE, ...)
## S3 method for class 'integer'
weighted.logloss(actual, response, w, normalize = TRUE, ...)

Arguments

actual

A vector length $n$ , and $k$ levels. Can be of integer or factor.

response

w

A <double> vector of sample weights.

normalize

A <logical>-value (default: TRUE). If TRUE, the mean cross-entropy across all observations is returned; otherwise, the sum of cross-entropies is returned.

...

Arguments passed into other methods.

Value

A <double>

References

MacKay, David JC. Information theory, inference and learning algorithms. Cambridge university press, 2003.

Kramer, Oliver, and Oliver Kramer. "Scikit-learn." Machine learning for evolution strategies (2016): 45-53.

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted response
## probabilities
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

response <- runif(n = 1e3)

## Generate sample
## weights
sample_weights <- runif(
  n = 1e3
)


## Evaluate performance
SLmetrics::weighted.logloss(
   actual    = actual_classes, 
   response  = cbind(
     response,
     1 - response
   ),
   w = sample_weights
)

## Generate observed
## frequencies 
actual_frequency <- sample(10L:100L, size = 1e3, replace = TRUE)

SLmetrics::weighted.logloss(
   actual    = actual_frequency, 
   response  = response,
   w         = sample_weights
)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted response
## probabilities
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

response <- runif(n = 1e3)

## Generate sample
## weights
sample_weights <- runif(
  n = 1e3
)


## Evaluate performance
SLmetrics::weighted.logloss(
   actual    = actual_classes, 
   response  = cbind(
     response,
     1 - response
   ),
   w = sample_weights
)

## Generate observed
## frequencies 
actual_frequency <- sample(10L:100L, size = 1e3, replace = TRUE)

SLmetrics::weighted.logloss(
   actual    = actual_frequency, 
   response  = response,
   w         = sample_weights
)

Mean Arctangent Absolute Percentage Error

Description

Defensive measures

To guard against this, wrap maape() in a "safe" validator that checks for NA values and matching length, for example:

safe_maape <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  maape(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.maape(actual, predicted, w, ...)
## S3 method for class 'numeric'
weighted.maape(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.maape(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.maape(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

Mean Absolute Error

Description

Defensive measures

To guard against this, wrap mae() in a "safe" validator that checks for NA values and matching length, for example:

safe_mae <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  mae(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.mae(actual, predicted, w, ...)
## S3 method for class 'numeric'
weighted.mae(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.mae(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.mae(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

Mean Absolute Percentage Error

Description

Defensive measures

To guard against this, wrap mape() in a "safe" validator that checks for NA values and matching length, for example:

safe_mape <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  mape(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.mape(actual, predicted, w, ...)
## S3 method for class 'numeric'
weighted.mape(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.mape(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.mape(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

Matthews Correlation Coefficient

Description

Defensive measures

To guard against this, wrap mcc() in a "safe" validator that checks for NA values and matching length, for example:

safe_mcc <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  mcc(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate matthews correlation coefficient
## via S3 dispatching
mcc(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
weighted.mcc(actual, predicted, w, ...)
## S3 method for class 'factor'
weighted.mcc(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

...

Arguments passed into other methods.

Value

A <double>-value

Other names

The Matthews Correlation Coefficient has other names depending on research field:

$\phi$ -coefficient, phi()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.mcc(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.mcc(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

Mean Percentage Error

Description

Defensive measures

To guard against this, wrap mpe() in a "safe" validator that checks for NA values and matching length, for example:

safe_mpe <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  mpe(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.mpe(actual, predicted, w, ...)
## S3 method for class 'numeric'
weighted.mpe(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.mpe(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.mpe(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

Mean Squared Error

Description

Defensive measures

To guard against this, wrap mse() in a "safe" validator that checks for NA values and matching length, for example:

safe_mse <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  mse(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.mse(actual, predicted, w, ...)
## S3 method for class 'numeric'
weighted.mse(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.mse(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.mse(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

Negative Likelihood Ratio

Description

Defensive measures

To guard against this, wrap nlr() in a "safe" validator that checks for NA values and matching length, for example:

safe_nlr <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  nlr(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate negative likelihood ratio
## via S3 dispatching
nlr(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
weighted.nlr(actual, predicted, w, ...)
## S3 method for class 'factor'
weighted.nlr(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.nlr(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.nlr(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

Negative Predictive Value

Description

Defensive measures

To guard against this, wrap npv() in a "safe" validator that checks for NA values and matching length, for example:

safe_npv <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  npv(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate negative predictive value
## via S3 dispatching
npv(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
weighted.npv(actual, predicted, w, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'factor'
weighted.npv(actual, predicted, w, estimator = 0L, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.npv(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.npv(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

Pinball Loss

Description

Defensive measures

To guard against this, wrap pinball() in a "safe" validator that checks for NA values and matching length, for example:

safe_pinball <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  pinball(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.pinball(actual, predicted, w, alpha = 0.5, deviance = FALSE, ...)
## S3 method for class 'numeric'
weighted.pinball(actual, predicted, w, alpha = 0.5, deviance = FALSE, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

alpha

A <double>-value of length $1$ (default: $0.5$ ). The slope of the pinball loss function.

deviance

A <logical>-value of length 1 (default: FALSE). If TRUE the function returns the $D^2$ loss.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.pinball(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.pinball(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

Positive Likelihood Ratio

Description

Defensive measures

To guard against this, wrap plr() in a "safe" validator that checks for NA values and matching length, for example:

safe_plr <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  plr(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate positive likelihood ratio
## via S3 dispatching
plr(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
weighted.plr(actual, predicted, w, ...)
## S3 method for class 'factor'
weighted.plr(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.plr(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.plr(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

Precision Recall Curve

Description

Defensive measures

To guard against this, wrap pr.curve() in a "safe" validator that checks for NA values and matching length, for example:

safe_pr.curve <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  pr.curve(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Area under the curve

Use auc.pr.curve for calculating the area under the curve directly.

Efficient multi-metric evaluation

## presort response
## probabilities
indices <- preorder(response, decreasing = TRUE)

## evaluate precision recall curve
pr.curve(actual, response, indices = indices)

Usage

## S3 method for class 'factor'
weighted.pr.curve(actual, response, w, thresholds = NULL, indices = NULL, ...)
## S3 method for class 'factor'
weighted.pr.curve(actual, response, w, thresholds = NULL, indices = NULL, ...)

Arguments

actual

A vector length $n$ , and $k$ levels. Can be of integer or factor.

response

w

A <double> vector of sample weights.

thresholds

An optional <double> vector of length $n$ (default: NULL).

indices

An optional $n \times k$ matrix of <integer> values of sorted response probability indices.

...

Arguments passed into other methods.

Value

A data.frame on the following form,

threshold

<numeric> Thresholds used to determine recall() and precision()

level

<character> The level of the actual <factor>

label

<character> The levels of the actual <factor>

recall

<numeric> The recall

precision

<numeric> The precision

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)


sample_weights <- runif(1e2)


## Visualize

plot(
    SLmetrics::weighted.pr.curve(
     actual   = actual_classes, 
     response = probability_matrix,
     w        = sample_weights
 )
)




## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)


sample_weights <- runif(1e2)


## Visualize

plot(
    SLmetrics::weighted.pr.curve(
     actual   = actual_classes, 
     response = probability_matrix,
     w        = sample_weights
 )
)

Precision

Description

Defensive measures

To guard against this, wrap precision() in a "safe" validator that checks for NA values and matching length, for example:

safe_precision <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  precision(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate precision
## via S3 dispatching
precision(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
weighted.precision(actual, predicted, w, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'factor'
weighted.precision(actual, predicted, w, estimator = 0L, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The precision has other names depending on research field:

Positive Predictive Value, ppv()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.precision(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.precision(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

Relative Absolute Error

Description

Defensive measures

To guard against this, wrap rae() in a "safe" validator that checks for NA values and matching length, for example:

safe_rae <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rae(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.rae(actual, predicted, w, ...)
## S3 method for class 'numeric'
weighted.rae(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.rae(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.rae(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

Recall

Description

Defensive measures

To guard against this, wrap recall() in a "safe" validator that checks for NA values and matching length, for example:

safe_recall <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  recall(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate recall
## via S3 dispatching
recall(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
weighted.recall(actual, predicted, w, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'factor'
weighted.recall(actual, predicted, w, estimator = 0L, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The Recall has other names depending on research field:

Sensitivity, sensitivity()
True Positive Rate, tpr()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.recall(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.recall(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

Root Mean Squared Error

Description

Defensive measures

To guard against this, wrap rmse() in a "safe" validator that checks for NA values and matching length, for example:

safe_rmse <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rmse(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.rmse(actual, predicted, w, ...)
## S3 method for class 'numeric'
weighted.rmse(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.rmse(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.rmse(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

Root Mean Squared Logarithmic Error

Description

Defensive measures

To guard against this, wrap rmsle() in a "safe" validator that checks for NA values and matching length, for example:

safe_rmsle <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rmsle(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.rmsle(actual, predicted, w, ...)
## S3 method for class 'numeric'
weighted.rmsle(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.rmsle(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.rmsle(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

Reciever Operator Characteristics

Description

Defensive measures

To guard against this, wrap roc.curve() in a "safe" validator that checks for NA values and matching length, for example:

safe_roc.curve <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  roc.curve(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Area under the curve

Use auc.roc.curve for calculating the area under the curve directly.

Efficient multi-metric evaluation

## presort response
## probabilities
indices <- preorder(response, decreasing = TRUE)

## evaluate reciever operator characteristics
roc.curve(actual, response, indices = indices)

Usage

## S3 method for class 'factor'
weighted.roc.curve(actual, response, w, thresholds = NULL, indices = NULL, ...)
## S3 method for class 'factor'
weighted.roc.curve(actual, response, w, thresholds = NULL, indices = NULL, ...)

Arguments

actual

A vector length $n$ , and $k$ levels. Can be of integer or factor.

response

w

A <double> vector of sample weights.

thresholds

An optional <double> vector of length $n$ (default: NULL).

indices

An optional $n \times k$ matrix of <integer> values of sorted response probability indices.

...

Arguments passed into other methods.

Value

A data.frame on the following form,

threshold

<numeric> Thresholds used to determine tpr() and fpr()

level

<character> The level of the actual <factor>

label

<character> The levels of the actual <factor>

fpr

<numeric> The false positive rate

tpr

<numeric> The true positve rate

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)


sample_weights <- runif(1e2)


## Visualize

plot(
    SLmetrics::weighted.roc.curve(
     actual   = actual_classes, 
     response = probability_matrix,
     w        = sample_weights
 )
)




## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual classes
## and response probabilities
actual_classes <- factor(
    x = sample(
      x = classes, 
      size = 1e2, 
      replace = TRUE, 
      prob = c(0.7, 0.3)
    )
)

response_probabilities <- ifelse(
    actual_classes == "Kebab", 
    rbeta(sum(actual_classes == "Kebab"), 2, 5), 
    rbeta(sum(actual_classes == "Falafel"), 5, 2)
)

## Construct response
## matrix
probability_matrix <- cbind(
    response_probabilities,
    1 - response_probabilities
)


sample_weights <- runif(1e2)


## Visualize

plot(
    SLmetrics::weighted.roc.curve(
     actual   = actual_classes, 
     response = probability_matrix,
     w        = sample_weights
 )
)

Relative Root Mean Squared Error

Description

Defensive measures

To guard against this, wrap rrmse() in a "safe" validator that checks for NA values and matching length, for example:

safe_rrmse <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rrmse(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.rrmse(actual, predicted, w, normalization = 1L, ...)
## S3 method for class 'numeric'
weighted.rrmse(actual, predicted, w, normalization = 1L, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

normalization

A <double>-value of length $1$ (default: $1$ ). $0$ : mean-normalization, $1$ : range-normalization, $2$ : IQR-normalization.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.rrmse(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.rrmse(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

Root Relative Squared Error

Description

Defensive measures

To guard against this, wrap rrse() in a "safe" validator that checks for NA values and matching length, for example:

safe_rrse <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rrse(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.rrse(actual, predicted, w, ...)
## S3 method for class 'numeric'
weighted.rrse(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.rrse(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.rrse(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

$r^2$

Description

Defensive measures

To guard against this, wrap rsq() in a "safe" validator that checks for NA values and matching length, for example:

safe_rsq <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rsq(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.rsq(actual, predicted, w, k = 0, ...)
## S3 method for class 'numeric'
weighted.rsq(actual, predicted, w, k = 0, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

k

A <double>-vector of length 1 (default: 0). For adjusted $R^2$ set $k = \kappa - 1$ , where $\kappa$ is the number of parameters.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.rsq(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.rsq(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

Symmetric Mean Absolutte Percentage Error

Description

Defensive measures

To guard against this, wrap smape() in a "safe" validator that checks for NA values and matching length, for example:

safe_smape <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  smape(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

## S3 method for class 'numeric'
weighted.smape(actual, predicted, w, ...)
## S3 method for class 'numeric'
weighted.smape(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <double> vectors of length $n$ .

w

A <double> vector of sample weights.

...

Arguments passed into other methods

Value

A <double> value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.smape(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.smape(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)

Specificity

Description

Defensive measures

To guard against this, wrap specificity() in a "safe" validator that checks for NA values and matching length, for example:

safe_specificity <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  specificity(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate specificity
## via S3 dispatching
specificity(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
weighted.specificity(actual, predicted, w, estimator = 0L, na.rm = TRUE, ...)
## S3 method for class 'factor'
weighted.specificity(actual, predicted, w, estimator = 0L, na.rm = TRUE, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

estimator

An <integer>-value of length $1$ (default: $0$ ).

0 - a named <double>-vector of length k (class-wise)
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

na.rm

...

Arguments passed into other methods.

Value

If estimator is given as

0 - a named <double> vector of length k
1 - a <double> value (Micro averaged metric)
2 - a <double> value (Macro averaged metric)

Other names

The specificity has other names depending on research field:

True Negative Rate, tnr()
Selectivity, selectivity()

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.specificity(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.specificity(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

Zero-One Loss

Description

A generic S3 function to compute the zero-one loss score for a classification model. This function dispatches to S3 methods in zerooneloss() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because zerooneloss() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap zerooneloss() in a "safe" validator that checks for NA values and matching length, for example:

safe_zerooneloss <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  zerooneloss(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate zero-one loss
## via S3 dispatching
zerooneloss(confusion_matrix)

## additional performance metrics
## below

The zerooneloss.factor() method calls cmatrix() internally, so explicitly invoking zerooneloss.cmatrix() yourself avoids duplicate computation, yielding significant speed and memory effciency gains when you need multiple evaluation metrics.

Usage

## S3 method for class 'factor'
weighted.zerooneloss(actual, predicted, w, ...)
## S3 method for class 'factor'
weighted.zerooneloss(actual, predicted, w, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

w

A <double> vector of sample weights.

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.zerooneloss(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.zerooneloss(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)

Wine quality dataset

Description

This dataset contains measurements of various chemical properties of white wines along with their quality ratings and a quality classification. The dataset was obtained from the UCI Machine Learning Repository.

The data is provided as a list with two components:

features

A data frame containing the chemical properties of the wines. The variables include:

fixed_acidity: Fixed acidity (g/L).
volatile_acidity: Volatile acidity (g/L), mainly due to acetic acid.
citric_acid: Citric acid (g/L).
residual_sugar: Residual sugar (g/L).
chlorides: Chloride concentration (g/L).
free_sulfur_dioxide: Free sulfur dioxide (mg/L).
total_sulfur_dioxide: Total sulfur dioxide (mg/L).
density: Density of the wine (g/cm $^3$ ).
pH: pH value of the wine.
sulphates: Sulphates (g/L).
alcohol: Alcohol content (% by volume).

target

A list containing two elements:

regression

A numeric vector representing the wine quality scores (used as the regression target).

class

A factor with levels "High Quality", "Medium Quality", and "Low Quality", where classification is determined as follows:

High Quality: quality $\geq$ 7.
Low Quality: quality $\leq$ 4.
Medium Quality: for all other quality scores.

Usage

data(wine.quality)
data(wine.quality)

Format

A list with two components:

features: A data frame with 11 chemical property variables.
target: A list with two elements: regression (wine quality scores) and class (quality classification).

References

Cortez, Paulo, et al. "Modeling wine preferences by data mining from physicochemical properties." Decision support systems 47.4 (2009): 547-553.

Zero-One Loss

Description

Defensive measures

To guard against this, wrap zerooneloss() in a "safe" validator that checks for NA values and matching length, for example:

safe_zerooneloss <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  zerooneloss(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate zero-one loss
## via S3 dispatching
zerooneloss(confusion_matrix)

## additional performance metrics
## below

Usage

## Generic S3 method
## for Zero-One Loss
zerooneloss(...)

## Generic S3 method
## for weighted Zero-One Loss
weighted.zerooneloss(...)
## Generic S3 method
## for Zero-One Loss
zerooneloss(...)

## Generic S3 method
## for weighted Zero-One Loss
weighted.zerooneloss(...)

Arguments

...

Arguments passed on to zerooneloss.factor, weighted.zerooneloss.factor, zerooneloss.cmatrix

actual,predicted: A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.
w: A <double> vector of sample weights.
x: A confusion matrix created cmatrix().

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::zerooneloss(
   actual    = actual_classes, 
   predicted = predicted_classes
)

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::zerooneloss(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Zero-One Loss

Description

Defensive measures

To guard against this, wrap zerooneloss() in a "safe" validator that checks for NA values and matching length, for example:

safe_zerooneloss <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  zerooneloss(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate zero-one loss
## via S3 dispatching
zerooneloss(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'cmatrix'
zerooneloss(x, ...)
## S3 method for class 'cmatrix'
zerooneloss(x, ...)

Arguments

x

A confusion matrix created cmatrix().

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::zerooneloss(confusion_matrix)


## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
    actual    = actual_classes,
    predicted = predicted_classes
)

## Evaluate performance
SLmetrics::zerooneloss(confusion_matrix)

Zero-One Loss

Description

Defensive measures

To guard against this, wrap zerooneloss() in a "safe" validator that checks for NA values and matching length, for example:

safe_zerooneloss <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  zerooneloss(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Efficient multi-metric evaluation

## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate zero-one loss
## via S3 dispatching
zerooneloss(confusion_matrix)

## additional performance metrics
## below

Usage

## S3 method for class 'factor'
zerooneloss(actual, predicted, ...)
## S3 method for class 'factor'
zerooneloss(actual, predicted, ...)

Arguments

actual, predicted

A pair of <integer> or <factor> vectors of length $n$ , and $k$ levels.

...

Arguments passed into other methods.

Value

A <double>-value

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

Examples

## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::zerooneloss(
   actual    = actual_classes, 
   predicted = predicted_classes
)



## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
    x = sample(x = classes, size = 1e3, replace = TRUE),
    levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::zerooneloss(
   actual    = actual_classes, 
   predicted = predicted_classes
)

Package 'SLmetrics'

Help Index

Accuracy

Description

Defensive measures

Efficient multi-metric evaluation

Usage

Arguments

Value

References

See Also

Examples

Accuracy

Description

Defensive measures

Efficient multi-metric evaluation

Usage

Arguments

Value

References

See Also

Examples

Accuracy

Description

Defensive measures

Efficient multi-metric evaluation

Usage

Arguments

Value

References

See Also

Examples

Area under the Precision Recall Curve

Description

Defensive measures

Visualizing area under the precision recall curve

Efficient multi-metric evaluation

Usage

Arguments

Value

References

See Also

Examples

Area under the Precision Recall Curve

Description

Defensive measures

Visualizing area under the precision recall curve

Efficient multi-metric evaluation

Usage

Arguments

Value

References

See Also

Examples

Area under the Receiver Operator Characteristics Curve

Description

Defensive measures

Visualizing area under the receiver operator characteristics curve

Efficient multi-metric evaluation

Usage

Arguments

Value

References

See Also

Examples

Area under the Receiver Operator Characteristics Curve

Description

Defensive measures

Visualizing area under the receiver operator characteristics curve

Efficient multi-metric evaluation

Usage

Arguments

Value

References

See Also

Examples

Area under the curve

Description

Usage

Arguments