SLmetrics (version 0.3-4)

cmatrix.factor: 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

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

Value

A named \(k\) x \(k\) <matrix>

Arguments

actual, predicted

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

...

Arguments passed into other methods.

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.

See Also

Other Classification: accuracy(), auc.pr.curve(), auc.roc.curve(), baccuracy(), brier.score(), ckappa(), cross.entropy(), dor(), fbeta(), fdr(), fer(), fmi(), fpr(), hammingloss(), jaccard(), logloss(), mcc(), nlr(), npv(), plr(), pr.curve(), precision(), recall(), relative.entropy(), roc.curve(), shannon.entropy(), specificity(), zerooneloss()

Other Supervised Learning: accuracy(), auc.pr.curve(), auc.roc.curve(), baccuracy(), brier.score(), ccc(), ckappa(), cross.entropy(), deviance.gamma(), deviance.poisson(), deviance.tweedie(), dor(), fbeta(), fdr(), fer(), fmi(), fpr(), gmse(), hammingloss(), huberloss(), jaccard(), logloss(), maape(), mae(), mape(), mcc(), mpe(), mse(), nlr(), npv(), pinball(), plr(), pr.curve(), precision(), rae(), recall(), relative.entropy(), rmse(), rmsle(), roc.curve(), rrmse(), rrse(), rsq(), shannon.entropy(), smape(), specificity(), zerooneloss()

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
)