weighted.cmatrix.factor.md

weighted.cmatrix.factor	R Documentation

Confusion Matrix

Description

A generic S3 function to compute the confusion matrix for a classification model. This function dispatches to S3 methods incmatrix() 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 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
)

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