When two models share the same accuracy but differ in confidence, choosing the overconfident one can be risky. Modern neural networks often output inflated probabilities, making decisions unreliable. Calibration reveals whether a model’s confidence matches real outcomes. If probabilities matter and both models perform equally, the better calibrated model is the safer choice.
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