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

rse(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).

Note

This measure is undefined for constant \(t\).

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(), rrse(), rsq(), sae(), smape(), srho(), sse()

Examples

set.seed(1) truth = 1:10 response = truth + rnorm(10) rse(truth, response)
#> [1] 0.06858916