Et Si? - Score
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The following is its documentation:. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative. The recall is intuitively the ability of the classifier to find all the positive samples.
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The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal.
The formula for the F1 score is:. In the multi-class and multi-label case, this is the average of the F1 score of each class with weighting depending on the average parameter. The F-beta score can be interpreted as a weighted harmonic mean of the precision and recall, where an F-beta score reaches its best value at 1 and worst score at 0.
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The F-beta score weights recall more than precision by a factor of beta. For other uses, see Pasi disambiguation.
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