Computes the weighted balanced accuracy, suitable for imbalanced data sets. It is defined analogously to the definition in sklearn.
First, the sample weights \(w\) are normalized per class: $$ \hat{w}_i = \frac{w_i}{\sum_j 1(y_j = y_i) w_i}. $$ The balanced accuracy is calculated as $$ \frac{1}{\sum_i \hat{w}_i} \sum_i 1(r_i = t_i) \hat{w}_i. $$
R6::R6Class() inheriting from Measure.
This measures can be retrieved from the dictionary mlr_measures:
mlr_measures$get("classif.bacc")
msr("classif.bacc")
Type: "classif"
Range: \([0, 1]\)
Minimize: FALSE
Required prediction: response
Dictionary of Measures: mlr_measures
as.data.table(mlr_measures) for a complete table of all (also dynamically created) Measure implementations.
Other classification measures:
mlr_measures_classif.acc,
mlr_measures_classif.auc,
mlr_measures_classif.ce,
mlr_measures_classif.costs,
mlr_measures_classif.dor,
mlr_measures_classif.fbeta,
mlr_measures_classif.fdr,
mlr_measures_classif.fnr,
mlr_measures_classif.fn,
mlr_measures_classif.fomr,
mlr_measures_classif.fpr,
mlr_measures_classif.fp,
mlr_measures_classif.logloss,
mlr_measures_classif.mcc,
mlr_measures_classif.npv,
mlr_measures_classif.ppv,
mlr_measures_classif.precision,
mlr_measures_classif.recall,
mlr_measures_classif.sensitivity,
mlr_measures_classif.specificity,
mlr_measures_classif.tnr,
mlr_measures_classif.tn,
mlr_measures_classif.tpr,
mlr_measures_classif.tp
Other multiclass classification measures:
mlr_measures_classif.acc,
mlr_measures_classif.ce,
mlr_measures_classif.costs,
mlr_measures_classif.logloss