Depending on the setting the label distribution is often imbalanced. To make results comparable we use undersampling where in each of the 100 iterations a new sample is drawn from the larger class while all instances of the smaller class are used. This accounts for a majority vote (MV) baseline that always yields an accuracy score of 0.50 as both classes have equal size.