F-beta Score
Binary classification measure defined with P as precision()
and R as
recall()
as
(1 + beta^2) * (P*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.
fbeta(truth, response, positive, beta = 1, na_value = NaN, ...)
truth |
( |
response |
( |
positive |
( |
beta |
( |
na_value |
( |
... |
( |
Performance value as numeric(1)
.
Type: "binary"
Range: [0, 1]
Minimize: FALSE
Required prediction: response
This measure is undefined if
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.
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")
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