mlr_measures_regr.msle: Mean Squared Log Error

In mlr3: Machine Learning in R - Next Generation

Mean Squared Log Error

Description

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,

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

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")

Parameters

Empty ParamSet

Meta Information

• Type: "regr"

• Range: [0, \infty)

• Minimize: TRUE

• Required prediction: response

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.

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.rmse, mlr_measures_regr.rmsle, mlr_measures_regr.sae, mlr_measures_regr.smape, mlr_measures_regr.srho, mlr_measures_regr.sse

mlr3 documentation built on Sept. 14, 2025, 1:08 a.m.