  In this paper, we extend our PrInDT method (Weihs & Buschfeld 2021a) towards
undersampling with different percentages of the smaller and the larger classes
(psmall and plarge), stratification of predictors, varying the prediction
threshold, and measuring variable importance in ensembles. An application of
these methods to a linguistic example suggests the following: 1. In
undersampling, a careful selection of the percentages plarge and psmall is
important for building models with high balanced accuracies; 2. Stratification
of predictors does not majorly enhance balanced accuracies; 3. Lowering the
prediction threshold for the smaller class turns out to be an alternative
method to undersampling because it increases the likelihood of the smaller
class being selected. Finally, we introduce a method for ranking predictor
importance that allows for a straightforward interpretation of the results.
