Metrics: fbeta_score – R documentation

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fbeta_score

F-beta Score

fbeta_score computes a weighted harmonic mean of Precision and Recall. The beta parameter controls the weighting.

fbeta_score(actual, predicted, beta = 1)

`actual`	The ground truth binary numeric vector containing 1 for the positive class and 0 for the negative class.
`predicted`	The predicted binary numeric vector containing 1 for the positive class and 0 for the negative class. Each element represents the prediction for the corresponding element in `actual`.
`beta`	A non-negative real number controlling how close the F-beta score is to either Precision or Recall. When `beta` is at the default of 1, the F-beta Score is exactly an equally weighted harmonic mean. The F-beta score will weight toward Precision when `beta` is less than one. The F-beta score will weight toward Recall when `beta` is greater than one.

actual <- c(1, 1, 1, 0, 0, 0)
predicted <- c(1, 0, 1, 1, 1, 1)
recall(actual, predicted)

Evaluation Metrics for Machine Learning

v0.1.4

BSD_3_clause + file LICENSE

Authors

Ben Hamner [aut, cph], Michael Frasco [aut, cre], Erin LeDell [ctb]

Initial release