Model Calibration

Calibrate Neural Classifier Confidence with Temperature Scaling

A neural classifier can choose the correct class often enough for an application while assigning probabilities that are too concentrated or too diffuse. Accuracy alone does not expose this mismatch. A system that acts differently at confidence thresholds also depends on the numerical probabilities attached to its predictions. Temperature scaling is a post-training calibration method that adjusts the sharpness of classifier logits with one positive scalar. For a fixed input, it preserves the ordering of logits, so the predicted class remains unchanged when ordinary argmax decoding is used. What changes is the probability distribution produced after softmax.