fbeta_score: F-beta Score

In Metrics: Evaluation Metrics for Machine Learning

Description

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

Usage

fbeta_score(actual, predicted, beta = 1)

Arguments

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.

See Also

precision recall

Examples

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

Example output

[1] 0.6666667

Metrics documentation built on May 1, 2019, 10:11 p.m.