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Kendall's tau

Source: R/MeasureSimple.R
mlr_measures_regr.ktau.Rd

Measure to compare true observed response with predicted response in regression tasks.

Details

Kendall's tau is defined as Kendall's rank correlation coefficient between truth and response. It is defined as $$ \tau = \frac{(\mathrm{number of concordant pairs)} - (\mathrm{number of discordant pairs)}}{\mathrm{(number of pairs)}} $$ Calls stats::cor() with method set to "kendall".

Note

The score function calls mlr3measures::ktau() 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("regr.ktau")
msr("regr.ktau")

Parameters

Empty ParamSet

Meta Information

  • Type: "regr"

  • Range: \([-1, 1]\)

  • 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 regression measures: mlr_measures_regr.bias, mlr_measures_regr.mae, mlr_measures_regr.mape, mlr_measures_regr.maxae, mlr_measures_regr.medae, mlr_measures_regr.medse, mlr_measures_regr.mse, mlr_measures_regr.msle, mlr_measures_regr.pbias, mlr_measures_regr.pinball, mlr_measures_regr.rae, mlr_measures_regr.rmse, mlr_measures_regr.rmsle, mlr_measures_regr.rrse, mlr_measures_regr.rse, mlr_measures_regr.sae, mlr_measures_regr.smape, mlr_measures_regr.srho, mlr_measures_regr.sse