Decision trees for binary classification variables grow equally with the Gini impurity measure and Pearson's chi-square test
by Johannes L. Grabmeier, Larry A. Lambe
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 2, No. 2, 2007

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.

Online publication date: Mon, 04-Jun-2007

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