Detecting Interesting Decision Rules in Tree Ensembles

摘要

Mechanisms are provided for detecting interesting decision rules from a set of decision rules in a tree ensemble. Each tree in the tree ensemble is traversed in order to assign each individual data record from a set of data records to an identified leaf node in each tree. Predicted values are determined for the tree ensemble based on predictions provided by each leaf node to which each individual data record is assigned. Interesting sub-indices for decision rules from the set of decision rules are determined and, for each decision rule corresponding to the leaf nodes in the tree ensemble, the sub-indices are combined into interestingness index I_t. The decision rules are ranked corresponding to the leaf nodes in the tree ensemble according to the associated value of the interestingness index I_t and a subset of the decision rules corresponding to the leaf nodes in the tree ensemble are reported.