A loss function is said to be a proper scoring rule if the probability distribution that minimizes the Expectation is the true distribution of the underlying variable we are trying to predict.
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A loss function L is said to be a proper scoring rule if the probability distribution μ that minimizes the Expectation E[L(μ)] is the true distribution of the underlying variable we are trying to predict.