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Binary classification is highly used in credit scoring in the estimation ofprobability of default. The validation of such predictive models is based bothon rank ability, and also on calibration (i.e. how accurately the probabilitiesoutput by the model map to the observed probabilities). In this study we coverthe current best practices regarding calibration for binary classification, andexplore how different approaches yield different results on real world creditscoring data. The limitations of evaluating credit scoring models using onlyrank ability metrics are explored. A benchmark is run on 18 real worlddatasets, and results compared. The calibration techniques used are PlattScaling and Isotonic Regression. Also, different machine learning models areused: Logistic Regression, Random Forest Classifiers, and Gradient BoostingClassifiers. Results show that when the dataset is treated as a time series,the use of re-calibration with Isotonic Regression is able to improve the longterm calibration better than the alternative methods. Using re-calibration, thenon-parametric models are able to outperform the Logistic Regression on BrierScore Loss.

Calibration of Machine Learning Classifiers for Probability of Default Modelling