Smooth isotonic regression: a new method to calibrate predictive models

Predictive models are critical for risk adjustment in clinical research. Evaluation of supervised learning models often focuses on predictive model discrimination, sometimes neglecting the assessment of their calibration. Recent research in machine learning has shown the benefits of calibrating predictive models, which becomes especially important when probability estimates are used for clinical decision making. By extending the isotonic regression method for recalibration to obtain a smoother fit in reliability diagrams, we introduce a novel method that combines parametric and non-parametric approaches. The method calibrates probabilistic outputs smoothly and shows better generalization ability than its ancestors in simulated as well as real world biomedical data sets.