Scoring rule

Common card · No. 268199

In decision theory, both a scoring rule as well as a scoring function provide an ex post summary measure for the evaluation of the quality of a prediction or forecast. They assign a numeric score to a single prediction given the actual outcome. Depending on the sign convention, this score can be interpreted as a loss or a reward for the forecaster. Scoring rules assess probabilistic predictions or forecasts, i.e. predictions of the whole probability distribution F {\displaystyle F} of the outcome. On the other hand, scoring functions assess point predictions, i.e. predictions of a property or functional T ( F ) {\displaystyle T(F)} of the probability distribution F {\displaystyle F} of the outcome. Examples of such a property are the expectation and the median. Scoring rules answer the question "how good is a predicted probability distribution...

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