Confidence Calibration

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Confidence calibration matters when people use a model’s confidence score to make real choices. A system can be accurate overall and still report confidence in a misleading way. For example, it might say “90% sure” far more often than it should. When that happens, teams may escalate the wrong cases or trust outputs that deserve a second look.

Calibration checks whether confidence scores match reality over many predictions. Teams often visualize this with reliability curves that compare stated confidence to actual accuracy. Calibration should also be tested under the same kinds of shifts the system will face in real use, since confidence can drift when data changes. This is what separates a confidence score that looks good in a report from one that stays useful in production.

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