  A set of probabilistic predictions is well calibrated if the events that are
predicted to occur with probability p do in fact occur about p fraction of the
time. Well calibrated predictions are particularly important when machine
learning models are used in decision analysis. This paper presents two new
non-parametric methods for calibrating outputs of binary classification models:
a method based on the Bayes optimal selection and a method based on the
Bayesian model averaging. The advantage of these methods is that they are
independent of the algorithm used to learn a predictive model, and they can be
applied in a post-processing step, after the model is learned. This makes them
applicable to a wide variety of machine learning models and methods. These
calibration methods, as well as other methods, are tested on a variety of
datasets in terms of both discrimination and calibration performance. The
results show the methods either outperform or are comparable in performance to
the state-of-the-art calibration methods.
