Scoring rule

（重定向自Proper scoring rule）

Expected value of Logarithmic rule, when Event 1 is expected to occur with probability of 0.8

Score of a binary classification for the true class showing logarithmic (blue), spherical (green), and quadratic (red)

Normalized score of a binary classification for the true class showing logarithmic (blue), spherical (green), and quadratic (red)

In decision theory, a score function, or scoring rule, measures the accuracy of probabilistic predictions. It is applicable to tasks in which predictions must assign probabilities to a set of mutually exclusive discrete outcomes. 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). A score can be thought of as either a measure of the "calibration" of a set of probabilistic predictions, or as a "cost function" or "loss function".

单词	Proper scoring rule
释义	Proper scoring rule 英语百科 Scoring rule （重定向自Proper scoring rule） In decision theory, a score function, or scoring rule, measures the accuracy of probabilistic predictions. It is applicable to tasks in which predictions must assign probabilities to a set of mutually exclusive discrete outcomes. 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). A score can be thought of as either a measure of the "calibration" of a set of probabilistic predictions, or as a "cost function" or "loss function".
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