mlr3measures (version 0.3.0)

rrse: Root Relative Squared Error

Description

Regression measure defined as $$ \sqrt{\frac{\sum_{i=1}^n \left( t_i - r_i \right)^2}{\sum_{i=1}^n \left( t_i - \bar{t} \right)^2}}. $$ Can be interpreted as root of the squared error of the predictions relative to a naive model predicting the mean.

Usage

rrse(truth, response, na_value = NaN, ...)

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.

na_value

:: numeric(1) Value that should be returned if the measure is not defined for the input (as described in the note). Default is NaN.

...

:: any Additional arguments. Currently ignored.

Value

Performance value as numeric(1).

Meta Information

• Type: "regr"

• Range: \([0, \infty)\)

• Minimize: TRUE

• Required prediction: response

See Also

Other Regression Measures: bias(), ktau(), mae(), mape(), maxae(), maxse(), medae(), medse(), mse(), msle(), pbias(), rae(), rmse(), rmsle(), rse(), rsq(), sae(), smape(), srho(), sse()

Examples

# NOT RUN {
set.seed(1)
truth = 1:10
response = truth + rnorm(10)
rrse(truth, response)
# }