👉 Calibration math is a statistical process used to fine-tune machine learning models, particularly in the context of regression tasks, to ensure that the model's predicted probabilities closely match the actual frequencies of the target variable. The goal is to adjust the model's output probabilities so that they reflect the true likelihood of outcomes, rather than just the model's internal bias or variance. This is achieved by iteratively adjusting the model's parameters to minimize the difference between predicted probabilities and the observed frequencies in a calibration set. Common methods include Platt scaling, isotonic regression, and beta calibration, each employing different mathematical techniques to achieve this alignment between predicted probabilities and real-world outcomes.
