fbeta | R Documentation |
Measure to compare true observed labels with predicted labels in binary classification tasks.
fbeta(truth, response, positive, beta = 1, na_value = NaN, ...)
truth |
( |
response |
( |
positive |
( |
beta |
( |
na_value |
( |
... |
( |
With P as precision()
and R as recall()
, the F-beta Score is defined 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.
This measure is undefined if precision or recall is undefined, i.e. TP + FP = 0 or TP + FN = 0.
Performance value as numeric(1)
.
Type: "binary"
Range: [0, 1]
Minimize: FALSE
Required prediction: response
Rijsbergen, Van CJ (1979). Information Retrieval, 2nd edition. Butterworth-Heinemann, Newton, MA, USA. ISBN 408709294.
Goutte C, Gaussier E (2005). “A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation.” In Lecture Notes in Computer Science, 345–359. doi: 10.1007/978-3-540-31865-1_25.
Other Binary Classification Measures:
auc()
,
bbrier()
,
dor()
,
fdr()
,
fnr()
,
fn()
,
fomr()
,
fpr()
,
fp()
,
mcc()
,
npv()
,
ppv()
,
prauc()
,
tnr()
,
tn()
,
tpr()
,
tp()
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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