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Isotonic Calibration of Classifier Scores

Medium · machine_learning · Quant Researcher interview question · machine-learning, calibration, isotonic-regression, pav, scipy

Probability calibration transforms classifier outputs into reliable probabilities, a crucial step for using machine learning models as signal generators in quantitative trading. Isotonic regression, implemented via the Pool Adjacent Violators (PAV) algorithm, provides a non-parametric method to enforce a monotonic relationship between model scores and empirical outcomes. This ensures that a higher score consistently corresponds to a higher probability of a positive event. Task Implement solutio