Measure to compare true observed response with predicted response in regression tasks.
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():
mlr_measures$get("regr.msle")
msr("regr.msle")
Empty ParamSet
Type: "regr"
Range: \([0, \infty)\)
Minimize: TRUE
Required prediction: response
The Mean Squared Log Error is defined as $$ \frac{1}{n} \sum_{i=1}^n w_i \left( \ln (1 + t_i) - \ln (1 + r_i) \right)^2, $$ where \(w_i\) are normalized sample weights. This measure is undefined if any element of \(t\) or \(r\) is less than or equal to \(-1\).
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.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