Informed decision-making based on classifiers requires that the confidence in their predictions reflect the actual error rates. When this happens, one speaks of a calibrated model. Recent work showed that expressive neural networks are able to overfit the cross-entropy loss without losing accuracy, thus producing overconfident (i.e. miscalibrated) models. We analyse several definitions of calibration and the relationships between them, look into related empirical measures and their usefulness, and explore several algorithms to improve calibration.