The FOREST Procedure

To predict an observation, the FOREST procedure first assigns the observation to a single leaf in each decision tree in the forest, then uses that leaf to make a prediction based on the tree that contains the leaf, and finally simply averages the predictions over the trees. For an interval target, the prediction in a leaf equals the average of the target values among the bagged training observations in that leaf. For a nominal target, the posterior probability of a target category equals the proportion of that category among the bagged training observations in that leaf. The predicted nominal target category is the category that has the largest posterior probability. In case of a tie, the first category that occurs in the training data is the prediction.