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.


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.


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


Meta Information

  • Type: "binary"

  • Range: \([0, 1]\)

  • Minimize: FALSE

  • Required prediction: response

See also