CalibratedClassifierCV

CalibratedClassifierCV is a meta-estimator in scikit-learn that wraps an existing classifier to improve the reliability of its probability estimates. Many classifiers, such as Support Vector Machines (SVMs) or tree-based models, produce probability scores that are not well-calibrated, meaning they do not accurately reflect the true likelihood of a class. CalibratedClassifierCV addresses this by fitting a calibrator model to the output of the base classifier.

The calibration process typically involves two steps: training the base classifier using cross-validation and then fitting a calibrator to the cross-validation predictions. The cross-validation ensures that the base classifier's predictions are not overly dependent on a specific training set. The calibrator then learns a mapping from the raw classifier outputs to calibrated probabilities.

There are two primary calibration methods available: 'sigmoid' (also known as Platt scaling) and 'isotonic' regression. Sigmoid scaling fits a logistic sigmoid function to the classifier's output, while isotonic regression fits a piecewise constant, non-decreasing function. The choice between these methods can depend on the characteristics of the base classifier's output.

By using CalibratedClassifierCV, users can obtain more trustworthy probability scores, which are crucial for applications where the confidence of a prediction is important, such as in risk assessment or decision-making processes. It effectively separates the task of classification from the task of probability calibration.