Figure 9.

Effect of various training parameters on the random forest model for the binary prediction problem. The minimum node size, features tried at every level of split, and the number of trees are varied and the error rates for the training and test split are plotted. In (A), a prediction is considered as an error if it does not agree with the expert’s primary diagnosis, and in (B), it is considered an error if the prediction does not agree with the primary or alternative diagnosis.