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.
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():
mlr_measures$get("fbeta")
msr("fbeta")
Type: "binary"
Range: \([0, 1]\)
Minimize: FALSE
Required prediction: response
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.mbrier,
mlr_measures_classif.mcc,
mlr_measures_classif.npv,
mlr_measures_classif.ppv,
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.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