mlr3measures (version 1.0.0)

ktau: Kendall's tau

Description

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

Usage

ktau(truth, response, ...)

Value

Performance value as numeric(1).

Arguments

truth

(numeric())
True (observed) values. Must have the same length as response.

response

(numeric())
Predicted response values. Must have the same length as truth.

...

(any)
Additional arguments. Currently ignored.

Meta Information

• Type: "regr"

• Range: \([-1, 1]\)

• Minimize: FALSE

• Required prediction: response

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".

References

Rosset S, Perlich C, Zadrozny B (2006). “Ranking-based evaluation of regression models.” Knowledge and Information Systems, 12(3), 331--353. tools:::Rd_expr_doi("10.1007/s10115-006-0037-3").

See Also

Other Regression Measures: ae(), ape(), bias(), linex(), mae(), mape(), maxae(), maxse(), medae(), medse(), mse(), msle(), pbias(), pinball(), rae(), rmse(), rmsle(), rrse(), rse(), rsq(), sae(), se(), sle(), smape(), srho(), sse()

Examples

set.seed(1)
truth = 1:10
response = truth + rnorm(10)
ktau(truth, response)