ckappa.md

ckappa	R Documentation

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 inckappa() 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

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 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(...)

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
)

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