R/classif_bacc.R

In mlr3measures: Performance Measures for 'mlr3'

#' @title Balanced Accuracy
#'
#' @details
#' The Balanced Accuracy computes the weighted balanced accuracy, suitable for imbalanced data sets.
#' It is defined analogously to the definition in [sklearn](https://scikit-learn.org/).
#'
#' First, the sample weights \eqn{w} are normalized per class:
#' \deqn{
#'  \hat{w}_i = \frac{w_i}{\sum_j 1(y_j = y_i) w_i}.
#' }{
#'  w_hat[i] = w[i] / sum((t == t[i]) * w[i]).
#' }
#' The balanced accuracy is calculated as
#' \deqn{
#'  \frac{1}{\sum_i \hat{w}_i} \sum_i 1(r_i = t_i) \hat{w}_i.
#' }{
#'  1 / sum(w_hat) * sum((r == t) * w_hat).
#' }
#'
#' @references
#' `r format_bib("brodersen_2010", "guyon_2015")`
#'
#' @templateVar mid bacc
#' @template classif_template
#'
#' @inheritParams classif_params
#' @template classif_example
#' @export
bacc = function(truth, response, sample_weights = NULL, ...) {
  assert_classif(truth, response = response)
  if (is.null(sample_weights)) {
    sample_weights = rep(1, length(truth))
  } else {
    assert_numeric(sample_weights, lower = 0, any.missing = FALSE)
  }

  label_weights = vapply(split(sample_weights, truth), sum, NA_real_)
  sample_weights = sample_weights / label_weights[truth]
  sample_weights[is.na(sample_weights)] = 0

  sum((truth == response) * sample_weights) / sum(sample_weights)
}

#' @include measures.R
add_measure(bacc, "Balanced Accuracy", "classif", 0, 1, FALSE)