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 It. The decision rules are ranked corresponding to the leaf nodes in the tree ensemble according to the associated value of the interestingness index It and a subset of the decision rules corresponding to the leaf nodes in the tree ensemble are reported.

主权项

1. A method, in a data processing system, for detecting interesting decision rules from a set of decision rules in a tree ensemble, the method comprising:
traversing each tree in the tree ensemble in order to assign each individual data record from a set of data records in an evaluation data set to an identified leaf node in a set of leaf nodes in each tree; determining predicted values defined by the tree ensemble based on predictions provided by each leaf node to which each individual data record is assigned; determining interesting sub-indices for decision rules from the set of decision rules corresponding to the leaf nodes in the tree ensemble; for each decision rule corresponding to the leaf nodes in the tree ensemble, combining the sub-indices into interestingness index It; ranking the decision rules corresponding to the leaf nodes in the tree ensemble according to the associated value of the interestingness index It; and reporting a subset of the decision rules corresponding to the leaf nodes in the tree ensemble in order to provide a notification of the interesting decision rules in the tree ensemble.