Description

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:

w_hat[i] = w[i] / sum((t == t[i]) * w[i]).

The balanced accuracy is calculated as

1 / sum(w_hat) * sum((r == t) * w_hat).

Dictionary

This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():

Meta Information

• Type: "classif"

• Range: [0, 1]

• Minimize: FALSE

• Required prediction: response

Note

The score function calls mlr3measures::bacc() from package mlr3measures.

If the measure is undefined for the input, NaN is returned. This can be customized by setting the field na_value.

See Also

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.bbrier, 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.mbrier, mlr_measures_classif.mcc, mlr_measures_classif.npv, mlr_measures_classif.ppv, mlr_measures_classif.prauc, 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, mlr_measures_classif.mbrier