  Boosted decision trees typically yield good accuracy, precision, and ROC
area. However, because the outputs from boosting are not well calibrated
posterior probabilities, boosting yields poor squared error and cross-entropy.
We empirically demonstrate why AdaBoost predicts distorted probabilities and
examine three calibration methods for correcting this distortion: Platt
Scaling, Isotonic Regression, and Logistic Correction. We also experiment with
boosting using log-loss instead of the usual exponential loss. Experiments show
that Logistic Correction and boosting with log-loss work well when boosting
weak models such as decision stumps, but yield poor performance when boosting
more complex models such as full decision trees. Platt Scaling and Isotonic
Regression, however, significantly improve the probabilities predicted by
