weighted.brier.score.matrix.md

weighted.brier.score.matrix	R Documentation

Brier Score

Description

A generic S3 function to compute the brier score score for a classification model. This function dispatches to S3 methods inbrier.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 preventR-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

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

Arguments

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.
...	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
)

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