  In this paper, we extend our PrInDT method (Weihs, Buschfeld 2021) towards
additional undersampling of one of the predictors. This helps us to handle
multiple unbalanced data sets, i.e. data sets that are not only unbalanced with
respect to the class variable but also in one of the predictor variables.
Beyond the advantages of such an approach, our study reveals that the balanced
accuracy in the full data set can be much lower than in the predictor
undersamples. We discuss potential reasons for this problem and draw
methodological conclusions for linguistic studies.
