Postcalibration

Postcalibration is a statistical technique used to adjust the predictions of a machine learning model to better align with the true underlying probabilities. This process is particularly important in applications where the cost of false positives and false negatives differs significantly, such as in medical diagnosis or fraud detection. By postcalibration, the model's output is transformed to more accurately reflect the true likelihood of an event occurring.

The most common methods of postcalibration include Platt scaling, isotonic regression, and Bayesian binning into quantiles (BBQ). Platt scaling involves fitting a logistic regression model to the model's output probabilities, while isotonic regression adjusts the probabilities using a non-parametric, piecewise-constant function. BBQ, on the other hand, divides the predicted probabilities into quantiles and estimates the true probabilities within each quantile.

Postcalibration can be performed using various techniques, such as cross-validation or a separate validation set. The choice of method depends on the specific characteristics of the data and the model. It is important to note that postcalibration should be applied to the entire dataset, including the training and test sets, to ensure that the model's performance is accurately assessed.

In summary, postcalibration is a valuable tool for improving the accuracy and reliability of machine learning models. By adjusting the model's output probabilities, postcalibration helps to better reflect the true likelihood of an event occurring, leading to more informed decision-making. However, it is essential to carefully select and apply the appropriate postcalibration method to achieve the best results.