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Sum of Squared Errors

Source: R/MeasureSimple.R
mlr_measures_regr.sse.Rd

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

Details

The Sum of Squared Errors is defined as $$ \sum_{i=1}^n w_i \left( t_i - r_i \right)^2. $$ where \(w_i\) are unnormalized weights for each observation \(x_i\), defaulting to 1.

Note

The score function calls mlr3measures::sse() 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.sse")
msr("regr.sse")

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.mse, mlr_measures_regr.msle, mlr_measures_regr.pbias, mlr_measures_regr.rmse, mlr_measures_regr.rmsle, mlr_measures_regr.sae, mlr_measures_regr.smape, mlr_measures_regr.srho