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Brier score gives a sense of calibration (whether the model's predicted probabilities equal observed probabilities, e.g. do 40% of observations with a score of .4 have a 1 label?)
The brier score is the mean squared error between the label and the risk score. If a model has 2000 predictions, it would be 1/2000 sum [(each prediction - each label)^2]
Brier score gives a sense of calibration (whether the model's predicted probabilities equal observed probabilities, e.g. do 40% of observations with a score of .4 have a 1 label?)
The brier score is the mean squared error between the label and the risk score. If a model has 2000 predictions, it would be 1/2000 sum [(each prediction - each label)^2]
sklearn function: http://scikit-learn.org/stable/modules/model_evaluation.html
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