Unverified50% confidenceFactExact time
Model calibration refers to the degree to which a model's output probabilities match its actual accuracy rate; for a well-calibrated model, if the probability it gives for a certain type of question is 0.8, then about 80% of such questions are indeed answered correctly.
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10/14/2026
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VerifiedIn a well-calibrated model, an output score of 0.8 should statistically correspond to an actual accuracy of 80%.80% similarUnverifiedCalibration means that when the model outputs 0.9, across a large number of similar samples about 90% are indeed the correct class; an uncalibrated model can have high AUC but its values cannot be used directly as probabilities.78% similarVerifiedModel calibration is defined in statistics as: if a model claims a certain answer has 70% confidence, then the actual proportion of correct answers among a large number of such claims should be close to 70%.78% similarUnverifiedIn a test of 12 models across 112 questions, when faced with a "Are you sure?" challenge, models had a 94.4% probability of maintaining their original correct answer.77% similarUnverifiedThe probability reported by a model (e.g., 90%) refers to the model's confidence in its own classification judgment and has nothing to do with business urgency.77% similar
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