Measure to compare true observed response with predicted response in regression tasks.
Usage
ktau(truth, response, ...)
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.
Value
Performance value as numeric(1).
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.
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)
#> [1] 0.8222222