Decision trees for binary classification variables grow equally with the Gini impurity measure and Pearson's chi-square test

Authors:
Johannes L. Grabmeier;Larry A. Lambe
Affiliations:
University of Applied Sciences Deggendorf, Edlmairstr. 6+8, D-94469, Deggendorf, Germany.;Multidisciplinary Software Systems Research Corporation (MSSRC), P.O. Box 6667, Bloomingdale, IL 60108, USA
Venue:
International Journal of Business Intelligence and Data Mining
Year:
2007

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Abstract

We show that for binary classification variables, Gini and Pearson purity measures yield exactly the same tree, provided all the other parameters of the algorithms are identical. A counter-example for ternary classification variables is given.