Maximizing Accuracy of Classification Trees by Optimal Pruning
Paul R. Yarnold, Ph.D., and Robert C. Soltysik, M.S.
Optimal Data Analysis, LLC
We describe a pruning methodology which maximizes effect strength for sensitivity of classification tree models. After deconstructing the initial “Bonferroni-pruned” model into all possible nested sub-branches, the sub-branch which explicitly maximizes mean sensitivity is identified. This methodology is illustrated using models predicting in-hospital mortality of 1,193 (Study 1) and 1,660 (Study 2) patients with AIDS-related Pneumocystis carinii pneumonia.