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.

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.

Dictionary

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

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

Meta Information

  • Type: "binary"

  • Range: \([0, 1]\)

  • Minimize: FALSE

  • Required prediction: response

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.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