Calibration
A family of metrics that measure the consistency between a model's output probabilities and its actual accuracy rates. Commonly quantified with the Brier score, a well-calibrated model produces confidence scores that align with its true accuracy on corresponding predictions.
Core Facts
Timeline (last 90 days)
The model showed the highest confidence (95%) on underdetermined problems (problems with no unique correct answer), exhibiting a reverse calibration phenomenon.
Calibration measures whether the confidence reported by the model matches actual accuracy; an unvalidated confidence interface may mislead downstream decisions and should not be shipped.
Calibration refers to the consistency between the probability estimates a model produces and the actual frequency of occurrence, and is commonly quantified using the Brier Score and calibration curves.
Each question in a prediction market settles only once, and a single outcome cannot distinguish between accurate prediction and good luck; statistically meaningful calibration comparisons require accumulating data across hundreds of settled questions.
It is recommended to set a confidence threshold in automated processes so that cases are automatically transferred to human handling when the model is uncertain.
All Facts (5)
Calibration refers to the consistency between the probability estimates a model produces and the actual frequency of occurrence, and is commonly quantified using the Brier Score and calibration curves.
65%UnverifiedThe model showed the highest confidence (95%) on underdetermined problems (problems with no unique correct answer), exhibiting a reverse calibration phenomenon.
50%UnverifiedCalibration measures whether the confidence reported by the model matches actual accuracy; an unvalidated confidence interface may mislead downstream decisions and should not be shipped.
50%UnverifiedEach question in a prediction market settles only once, and a single outcome cannot distinguish between accurate prediction and good luck; statistically meaningful calibration comparisons require accumulating data across hundreds of settled questions.
50%UnverifiedIt is recommended to set a confidence threshold in automated processes so that cases are automatically transferred to human handling when the model is uncertain.
50%