mlr3measures (version 0.3.1)

fbeta: F-beta Score

Description

Binary classification measure defined with \(P\) as precision() and \(R\) as recall() as $$ (1 + \beta^2) \frac{P \cdot R}{(\beta^2 P) + R}. $$ It measures the effectiveness of retrieval with respect to a user who attaches \(\beta\) times as much importance to recall as precision. For \(\beta = 1\), this measure is called "F1" score.

Usage

fbeta(truth, response, positive, beta = 1, na_value = NaN, ...)

Arguments

truth

(factor()) True (observed) labels. Must have the exactly same two levels and the same length as response.

response

(factor()) Predicted response labels. Must have the exactly same two levels and the same length as truth.

positive

(character(1)) Name of the positive class.

beta

(numeric(1)) Parameter to give either precision or recall more weight. Default is 1, resulting in balanced weights.

na_value

(numeric(1)) Value that should be returned if the measure is not defined for the input (as described in the note). Default is NaN.

...

(any) Additional arguments. Currently ignored.

Value

Performance value as numeric(1).

Meta Information

• Type: "binary"

• Range: \([0, 1]\)

• Minimize: FALSE

• Required prediction: response

References

Sasaki, Yutaka, others (2007). “The truth of the F-measure.” Teach Tutor mater, 1(5), 1--5. https://www.cs.odu.edu/~mukka/cs795sum10dm/Lecturenotes/Day3/F-measure-YS-26Oct07.pdf.

Rijsbergen, Van CJ (1979). Information Retrieval, 2nd edition. Butterworth-Heinemann, Newton, MA, USA. ISBN 408709294.

See Also

Other Binary Classification Measures: auc(), bbrier(), dor(), fdr(), fnr(), fn(), fomr(), fpr(), fp(), mcc(), npv(), ppv(), prauc(), tnr(), tn(), tpr(), tp()

Examples

# NOT RUN {
set.seed(1)
lvls = c("a", "b")
truth = factor(sample(lvls, 10, replace = TRUE), levels = lvls)
response = factor(sample(lvls, 10, replace = TRUE), levels = lvls)
fbeta(truth, response, positive = "a")
# }