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 $$ \frac{1}{\sum_i \hat{w}_i} \sum_i 1(r_i = t_i) \hat{w}_i. $$


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.


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



Empty ParamSet

Meta Information

  • Type: "classif"

  • Range: \([0, 1]\)

  • Minimize: FALSE

  • Required prediction: response

See also