Superfast Selection for Decision Tree Algorithms

Abstract

A method of decision tree split selection is provided. The method comprises scanning, in a data source, all values of a feature and example labels. Intermediate statistics information of size O(N×C) are prepared, wherein N is the number of unique values of the feature and C is the number of label …