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.


The score function calls mlr3measures::rrse() from package mlr3measures.

If the measure is undefined for the input, NaN is returned. This can be customized by setting the field na_value.


This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():


Meta Information

  • Type: "regr"

  • Range: \([0, \infty)\)

  • Minimize: TRUE

  • Required prediction: response

See also