  This paper studies theoretically and empirically a method of turning
machine-learning algorithms into probabilistic predictors that automatically
enjoys a property of validity (perfect calibration) and is computationally
efficient. The price to pay for perfect calibration is that these probabilistic
predictors produce imprecise (in practice, almost precise for large data sets)
probabilities. When these imprecise probabilities are merged into precise
probabilities, the resulting predictors, while losing the theoretical property
of perfect calibration, are consistently more accurate than the existing
methods in empirical studies.
