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Machine Learning Classic: Parsimonious Binary Classification Trees


Get your hands on a classic technical report outlining a three-step method of construction binary decision trees for multiple classification problems.



By Leo Breiman and Charles J. Stone.

A three-step method of construction binary decision trees for multiple classification problems is presented. First a splitting rule is defined in terms of a generalization of Gini’s index of diversity. Next the optimal termination rule is found relative to a criterion which penalizes both misclassifications and complex trees (i.e., those having many terminal nodes. The tree thus obtained depends on a complexity parameter which, in the final step is selected by data-splitting or cross-validation.


Decision tree example



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