This paper provides both an introduction to and a detailed overview of the principles and
practice of classifier calibration. A well-calibrated classifier correctly quantifies the level of
uncertainty or confidence associated with its instance-wise predictions. This is essential for
critical applications, optimal decision making, cost-sensitive classification, and for some
types of context change. Calibration research has a rich history which predates the birth of
machine learning as an academic field by decades. However, a recent increase in the …

This paper provides both an introduction to and a detailed overview of the principles and
practice of classifier calibration. A well-calibrated classifier correctly quantifies the level of
uncertainty or confidence associated with its instance-wise predictions. This is essential for
critical applications, optimal decision making, cost-sensitive classification, and for some
types of context change. Calibration research has a rich history which predates the birth of
machine learning as an academic field by decades. However, a recent increase in the …