Measure to compare true observed labels with predicted labels in multiclass classification tasks.
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")
Empty ParamSet
Type: "classif"
Range: \([0, 1]\)
Minimize: FALSE
Required prediction: response
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.
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