Machine Learning Classic: Parsimonious Binary Classification Trees

Previous post            Tweet Tags: Decision Trees, Leo Breiman, Machine Learning, Statistics 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. Previous post Most popular last 30 days Most viewed 7 Steps to Mastering Machine…


Link to Full Article: Machine Learning Classic: Parsimonious Binary Classification Trees