Mean Squared Error — mlr_measures_regr.mse • mlr3

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

Details

The Mean Squared Error is defined as $\frac{1}{n} \sum_{i = 1}^{n} w_{i} {(t_{i} - r_{i})}^{2},$ where $w_{i}$ are normalized sample weights.

Note

The score function calls mlr3measures::mse() 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():

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

Parameters

Empty ParamSet

Meta Information

Type: "regr"
Range: $[0, \infty)$
Minimize: TRUE
Required prediction: response

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.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.rse, mlr_measures_regr.sae, mlr_measures_regr.smape, mlr_measures_regr.srho, mlr_measures_regr.sse