Simple hypotheses testing for the number of trees in a random forest

In this study, we propose two informal hypothesis tests which may be useful in determining the number of trees in a random forest for use in classification. The first test declares that a case is 'easy' if the hypothesis of the equality of probabilities of two most popular classes is rejected. The second test declares that a case is 'hard' if the hypothesis that the relative difference or the margin of victory between the probabilities of two most popular classes is greater than or equal to some small number, say 0.05, is rejected. We propose to continue generating trees until all (or all but a small fraction) of the training cases are declared easy or hard. The advantage of combining the second test along with the first test is that the number of trees required to stop becomes much smaller than the first test only, where all (or all but a small fraction) of the training cases should be declared easy.