Predicting Good Probabilities With Supervised Learning
ing, bagged trees, random forests, boosted trees, boosted stumps, naive bayes and logistic regression. We show how maximum margin methods such as SVMs, boosted trees, and boosted stumps tend to push predicted probabilities away from 0 and 1. This hurts the quality of the probabili-ties they predict and yields a characteristic sigmoid-shaped
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