mlr3 (version 0.6.0)

mlr_measures_classif.bacc: Balanced Accuracy

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: $${\hat{w}}_i=\frac{w_i}{\sum _{j}1(y_j=y_i)w_i}.$$ The balanced accuracy is calculated as $$\frac{1}{\sum _i{\hat{w}}_i}\sum _{i}1(r_i=t_i){\hat{w}}_i.$$

Dictionary

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

mlr_measures$get("bacc")
msr("bacc")

Meta Information

• Type: "classif"

• Range: $[0,1]$

• Minimize: FALSE

• Required prediction: response

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.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