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The Bootstrap Forest platform predicts a response value by averaging the predicted response values across many decision trees. Each tree is grown on a bootstrap sample of the training data. A bootstrap sample is a random sample of observations, drawn with replacement. In addition, the predictors are sampled at each split in the decision tree. The decision tree is fit using the recursive partitioning methodology described in the Partition Models chapter.
3.
Repeat step 1 and step 2 until the number of trees specified in the Bootstrap Forest Specification window is reached or until Early Stopping occurs.

Help created on 3/19/2020