mlr_measures_classif.fbeta: F-beta Score

In mlr3: Machine Learning in R - Next Generation

F-beta Score

Description

Measure to compare true observed labels with predicted labels in binary classification tasks.

Details

With P as precision() and R as recall(), the F-beta Score is defined 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.

This measure is undefined if precision or recall is undefined, i.e. TP + FP = 0 or TP + FN = 0.

Dictionary

This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():

mlr_measures$get("classif.fbeta")
msr("classif.fbeta")

Parameters

Id	Type	Default	Range
beta	integer	-	[0, \infty)

Meta Information

• Type: "binary"

• Range: [0, 1]

• Minimize: FALSE

• Required prediction: response

Note

The score function calls mlr3measures::fbeta() from package mlr3measures.

If the measure is undefined for the input, NaN is returned. This can be customized by setting the field na_value.

See Also

Dictionary of Measures: mlr_measures

as.data.table(mlr_measures) for a complete table of all (also dynamically created) Measure implementations.

Other classification measures: mlr_measures_classif.acc, mlr_measures_classif.auc, mlr_measures_classif.bacc, mlr_measures_classif.bbrier, mlr_measures_classif.ce, mlr_measures_classif.costs, mlr_measures_classif.dor, mlr_measures_classif.fdr, mlr_measures_classif.fnr, mlr_measures_classif.fn, mlr_measures_classif.fomr, mlr_measures_classif.fpr, mlr_measures_classif.fp, mlr_measures_classif.logloss, mlr_measures_classif.mauc_au1p, mlr_measures_classif.mauc_au1u, mlr_measures_classif.mauc_aunp, mlr_measures_classif.mauc_aunu, mlr_measures_classif.mbrier, mlr_measures_classif.mcc, mlr_measures_classif.npv, mlr_measures_classif.ppv, mlr_measures_classif.prauc, mlr_measures_classif.precision, mlr_measures_classif.recall, mlr_measures_classif.sensitivity, mlr_measures_classif.specificity, mlr_measures_classif.tnr, mlr_measures_classif.tn, mlr_measures_classif.tpr, mlr_measures_classif.tp

Other binary classification measures: mlr_measures_classif.auc, mlr_measures_classif.bbrier, mlr_measures_classif.dor, mlr_measures_classif.fdr, mlr_measures_classif.fnr, mlr_measures_classif.fn, mlr_measures_classif.fomr, mlr_measures_classif.fpr, mlr_measures_classif.fp, mlr_measures_classif.mcc, mlr_measures_classif.npv, mlr_measures_classif.ppv, mlr_measures_classif.prauc, mlr_measures_classif.precision, mlr_measures_classif.recall, mlr_measures_classif.sensitivity, mlr_measures_classif.specificity, mlr_measures_classif.tnr, mlr_measures_classif.tn, mlr_measures_classif.tpr, mlr_measures_classif.tp

mlr3 documentation built on Nov. 17, 2023, 5:07 p.m.