Probability, Calibration, Machine Learning, Data Science, Model Tuning.
logits generated by a classifier into calibrated probabilities. The fundamental concept of Platt scaling involves fitting a logistic regression model to the output scores produced by the classifier. By optimizing the log-likelihood of the observed labels, the logistic regression model learns to map