Measure to compare true observed response with predicted response in regression tasks.
Details
The Mean Squared Log Error is defined as $$ \frac{1}{n} \sum_{i=1}^n w_i \left( \ln (1 + t_i) - \ln (1 + r_i) \right)^2. $$
This measure is undefined if any element of \(t\) or \(r\) is less than or equal to \(-1\).
Note
The score function calls mlr3measures::msle() 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.pbias,
mlr_measures_regr.rae,
mlr_measures_regr.rmse,
mlr_measures_regr.rmsle,
mlr_measures_regr.rrse,
mlr_measures_regr.rse,
mlr_measures_regr.rsq,
mlr_measures_regr.sae,
mlr_measures_regr.smape,
mlr_measures_regr.srho,
mlr_measures_regr.sse