 Not all classification models are naturally probabilistic, and some that are, notably naive Bayes classifiers, decision trees and boosting methods, produce distorted class probability distributions.In the case of decision trees, where is the proportion of training samples with label in the leaf where ends up, these distortions come about because learning algorithms such as C4.5 or CART explicitly aim to produce homogeneous leaves (giving probabilities close to zero or one, and thus high bias) while using few samples to estimate the relevant proportion (high variance).For the binary case, a common approach is to apply Platt scaling, which learns a logistic regression model on the scores.