Evaluating calibration performance
Evaluating the calibration performance of a classifier is crucial to assessing the reliability and accuracy of its probability estimates. Calibration evaluation allows us to determine how well the predicted probabilities align with the true probabilities or likelihoods of the predicted events. Here are some commonly used techniques for evaluating the calibration performance of classifiers:
- Calibration plot: A calibration plot visually assesses how well a classifier’s predicted probabilities match the true class frequencies. The x axis shows the predicted probabilities for each class, while the y axis shows the empirically observed frequencies for those predictions.
For a well-calibrated model, the calibration curve should closely match the diagonal, representing a 1:1 relationship between predicted and actual probabilities. Deviations from the diagonal indicate miscalibration, where the predictions are inconsistent with empirical evidence...
