mlr3 (version 0.23.0)

mlr_measures_regr.rse: Relative Squared Error

Description

Measure to compare true observed response with predicted response in regression tasks.

Dictionary

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

mlr_measures$get("regr.rse")
msr("regr.rse")

Parameters

Empty ParamSet

Meta Information

• Type: "regr"

• Range: $[0,\mathrm{\infty })$

• Minimize: TRUE

• Required prediction: response

Details

The Relative Squared Error is defined as $$\frac{\sum _{i=1}^n{(t_i-r_i)}^2}{\sum _{i=1}^n{(t_i-\overline{t})}^2},$$ where $\overline{t}=\sum _{i=1}^n{t}_i$.

Can be interpreted as squared error of the predictions relative to a naive model predicting the mean.

This measure is undefined for constant $t$.

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