Abstract
Various criteria have been proposed for deciding which split is best at a given node of a binary classification tree. Consider the question: given a goodness-of-split criterion and the class populations of the instances at a node, what distribution of the instances between the two children nodes maximizes the goodness-of-split criterion? The answers reveal an interesting distinction between the gini and entropy criterion. © 1996 Kluwer Academic Publishers,.
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APA
Breiman, L. (1996). Technical note: Some properties of splitting criteria. Machine Learning, 24(1), 41–47. https://doi.org/10.1007/bf00117831
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