Getting Started Guide


Decision Forest

The library provides decision forest classification and regression algorithms based on an ensemble of tree-structured classifiers (decision trees) built using the general technique of bootstrap aggregation (bagging) and random choice of features.
Decision tree
is a binary tree graph. Its internal (split) nodes represent a
decision function
used to select the following (child) node at the prediction stage. Its leaf (terminal) nodes represent the corresponding response values, which are the result of the prediction from the tree. For more details, see Classification and Regression > Decision Tree, [Breiman84] and [Breiman2001].
For more information on the concepts behind the algorithm, see "Details" section.
For more information on the algorithm's parameters for a specific computation mode and examples of its usage, see "Batch Processing", "Online Processing" and "Distributed Processing" sections.

Product and Performance Information


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