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Brier score
Measure of the accuracy of probabilistic predictions
Brier score
The Brier score is a strictly proper scoring rule that measures the accuracy of probabilistic predictions. For unidimensional predictions, it is strictly equivalent to the mean squared error as applied to predicted probabilities. The Brier score is applicable to tasks in which predictions must assign probabilities to a set of mutually exclusive discrete outcomes or classes. The set of possible outcomes can be either binary or categorical in nature, and the probabilities assigned to this set of outcomes must sum to one (where each individual probability is in the range of 0 to 1). It was proposed by Glenn W. Brier in 1950. The Brier score can be thought of as a cost function. More precisely, across all items i ∈ 1... N {\displaystyle i\in {1...N}} in a set of N predictions, the Brier score measures the mean squared difference between: The predicted probability assigned to the possible outcomes for...
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