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Balanced classification accuracy (bacc)

Source: R/pred_measure-builtin.R
measure_bacc.Rd

Computes balanced accuracy by averaging class-specific mean accuracy, giving each observed class equal weight regardless of class frequency.

Usage

measure_bacc(y, mupred, log_weights = NULL, pointwise = NULL)

Arguments

y

An integer vector of observed class labels. Code it 0 or 1 when mupred is a matrix, and 1 to K when mupred is a 3-D array.

mupred

A numeric array of posterior predictive means. For binary outcomes use a draws x observations matrix. For multiclass outcomes use a draws x observations x categories array.

log_weights

Optional numeric matrix of unnormalized log-importance weights with dimensions \(S \times n\). Weights are column-normalized before computing each per-observation contribution.

pointwise

Optional numeric vector of precomputed pointwise contributions \(acc_i / (K n_{y_i})\), as stored by measure_bacc(). If provided, mupred and log_weights are ignored. y is always required, because it defines the classes.

Value

An object of class "measure": a list with:

estimates

A 1 \(\times\) 2 matrix. The row is named after the measure, the columns are Estimate and SE (standard error).

pointwise

An \(n \times 1\) matrix of per-observation values. The column is named after the measure.

extra

Optional. Auxiliary data for the standard error of a difference. Most measures do not set it.

Attributes measure (i.e., measure name) and dims (draws \(\times\) observations) are also set. Use print() for a readable summary.

Examples

y <- c(1L, 1L, 2L, 2L)
mupred <- array(
  c(0.8, 0.2, 0.7, 0.3, 0.3, 0.7, 0.2, 0.8),
  dim = c(1, 4, 2)
)
measure_bacc(y, mupred)
#> 
#> Computed from 1 draws by 4 observations.
#> 
#>      Estimate    SE
#> bacc    0.500 0.250