Obtaining Calibrated Probabilities from Boosting

Understand

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.