The library provides decision forest classification and regression algorithms based on an ensemble of tree-structured classifiers, which are known as decision trees. Decision forest is built using the general technique of bagging, a

b

ootstrap

agg

regation, and a random choice of features.

Decision Tree is a binary tree graph. Its internal (split) nodes represent a

decision function

used to select the child node at the prediction stage. Its leaf, or terminal, nodes represent the corresponding response values, which are the result of the prediction from the tree. For more details, see [Breiman84] and [Breiman2001].