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 Regre...

This paper illustrates the philosophy which forms the basis of calibration exercises in general equilibriummacroeconomic models and the details of the procedure, the advantages and the disadvantages of the approach, with particular reference to the issue of testing \false" economic models. We provide an overview of the most recent simulation-based approaches to the testing problem and compare t...