Optimizing Suboptimal Classification Trees: Matlab® CART Model Predicting Probability of Lower Limb Prosthesis User’s Functional Potential

Paul R. Yarnold & Ariel Linden

Optimal Data Analysis, LLC & Linden Consulting Group, LLC

After any algorithm which controls the growth of a classification tree model has completed, the resulting model must be pruned in order to explicitly maximize predictive accuracy normed against chance. This article illustrates manually-conducted maximum-accuracy pruning of a classification and regression tree (CART) model that was developed to predict the functional capacity of lower limb prosthesis users.

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