Scoring rule

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In decision theory, a scoring rule is a measure of someone's performance at making predictions under uncertainty (which comprises two distinct aspects, calibration and discrimination). A proper scoring rule encourages the forecaster to be honest, as expected payoff is maximized by accurately reporting personal belief about the predicted event, rather than by gaming the system.

See also

• Technical explanation

References

• Tilmann Gneiting; Adrian E Raftery (March 2007). "Strictly Proper Scoring Rules, Prediction, and Estimation". Journal of the American Statistical Association 102 (477): 359-378.  (PDF)
• A Technical Explanation of Technical Explanation

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