Calibration, Data Science, Machine Learning, Supervised Learning, Regression.
the opposite bias and tend to push predictions closer to 0 and 1” (quote from «Predicting good probabilities with supervised learning»). Other use cases: Ensembling: when we want to combine many probability models having uncalibrated predictions makes a difference. : Sklearn random forest 1. random