Description Usage Arguments Value Author(s) See Also Examples
Brier score measures the accuracy of probabilistic predictions; the smaller the Brier score, the better. The Brier score always takes on a value between zero and one.
Given N predictions, the Brier score is defined by the mean squared difference between (1) the estimated probability assigned to the possible outcome and (2) the actual outcome.
Here is a few examples that help the interpretation of Brier score.
Suppose that one is forecasting the probability P that it will rain on a given day. Then the Brier score is calculated as follows:
If the forecast is 100% (P = 1) and it rains, then the Brier Score is 0, the best score achievable.
If the forecast is 100% and it does not rain, then the Brier Score is 1, the worst score achievable.
If the forecast is 70% (P = 0.70) and it rains, then the Brier Score is (0.70 − 1)^2 = 0.09.
If the forecast is 30% (P = 0.30) and it rains, then the Brier Score is (0.30 − 1)^2 = 0.49.
1 | mtr_brier_score(actual, predicted)
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actual |
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predicted |
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A scalar numeric output
An Chu
mtr_log_loss
mtr_mean_log_loss
1 2 3 | act <- c(0, 1, 1, 0)
pred <- c(0.1, 0.9, 0.8, 0.3)
mtr_brier_score(act, pred)
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