Measure to compare true observed response with predicted response in regression tasks.
Details
The Relative Squared Error is defined as $$ \frac{\sum_{i=1}^n \left( t_i - r_i \right)^2}{\sum_{i=1}^n \left( t_i - \bar{t} \right)^2}, $$ where \(\bar{t} = \sum_{i=1}^n t_i\).
Can be interpreted as squared error of the predictions relative to a naive model predicting the mean.
This measure is undefined for constant \(t\).
Note
The score function calls mlr3measures::rse() from package mlr3measures.
If the measure is undefined for the input, NaN is returned.
This can be customized by setting the field na_value.
Dictionary
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():
See also
Dictionary of Measures: mlr_measures
as.data.table(mlr_measures) for a complete table of all (also dynamically created) Measure implementations.
Other regression measures:
mlr_measures_regr.bias,
mlr_measures_regr.ktau,
mlr_measures_regr.mae,
mlr_measures_regr.mape,
mlr_measures_regr.maxae,
mlr_measures_regr.medae,
mlr_measures_regr.medse,
mlr_measures_regr.mse,
mlr_measures_regr.msle,
mlr_measures_regr.pbias,
mlr_measures_regr.pinball,
mlr_measures_regr.rae,
mlr_measures_regr.rmse,
mlr_measures_regr.rmsle,
mlr_measures_regr.rrse,
mlr_measures_regr.sae,
mlr_measures_regr.smape,
mlr_measures_regr.srho,
mlr_measures_regr.sse