Fits temperature scaling for probability calibration.
Uses a single temperature parameter T to soften/sharpen predictions.
Usage
fit_temperature_scaling(probs, labels)
Arguments
- probs
Calibration set predicted probabilities
- labels
Calibration set labels
Value
A function that recalibrates probabilities
Details
Temperature scaling modifies the logits by dividing by T:
$$p_{calibrated} = \sigma(logit(p) / T)$$
- T > 1: softens predictions (moves toward 0.5)
- T < 1: sharpens predictions (moves toward 0 or 1)
- T = 1: no change
Commonly used for neural network calibration.