mlr3 (version 1.0.0)

mlr_measures_classif.bacc: Balanced Accuracy

Description

Measure to compare true observed labels with predicted labels in multiclass classification tasks.

Dictionary

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

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

Parameters

Empty ParamSet

Meta Information

• Type: "classif"

• Range: $[0,1]$

• Minimize: FALSE

• Required prediction: response

Details

The Balanced Accuracy computes the weighted balanced accuracy, suitable for imbalanced data sets. It is defined analogously to the definition in sklearn.

First, all sample weights $w_i$ are normalized per class so that each class has the same influence: $${\hat{w}}_i=\frac{w_i}{\sum _{j=1}^n{w}_j\cdot \mathbf{1}(t_j=t_i)}.$$ The Balanced Accuracy is then calculated as $$\frac{1}{\sum _{i=1}^n{\hat{w}}_i}\sum _{i=1}^n{\hat{w}}_i\cdot \mathbf{1}(r_i=t_i).$$ This definition is equivalent to acc() with class-balanced sample weights.

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.fn, mlr_measures_classif.fnr, mlr_measures_classif.fomr, mlr_measures_classif.fp, mlr_measures_classif.fpr, mlr_measures_classif.logloss, mlr_measures_classif.mauc_au1p, mlr_measures_classif.mauc_au1u, mlr_measures_classif.mauc_aunp, mlr_measures_classif.mauc_aunu, mlr_measures_classif.mauc_mu, 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.tn, mlr_measures_classif.tnr, mlr_measures_classif.tp, mlr_measures_classif.tpr

Other multiclass classification measures: mlr_measures_classif.acc, mlr_measures_classif.ce, mlr_measures_classif.costs, mlr_measures_classif.logloss, mlr_measures_classif.mauc_au1p, mlr_measures_classif.mauc_au1u, mlr_measures_classif.mauc_aunp, mlr_measures_classif.mauc_aunu, mlr_measures_classif.mauc_mu, mlr_measures_classif.mbrier, mlr_measures_classif.mcc