mlr_measures_regr.rmsle: Root Mean Squared Log Error

In mlr3: Machine Learning in R - Next Generation

Root Mean Squared Log Error

Description

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

Details

The Root Mean Squared Log Error is defined as

\sqrt{\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.rmsle")
msr("regr.rmsle")

Parameters

Empty ParamSet

Meta Information

• Type: "regr"

• Range: [0, \infty)

• Minimize: TRUE

• Required prediction: response

Note

The score function calls mlr3measures::rmsle() 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.msle, mlr_measures_regr.pbias, mlr_measures_regr.pinball, mlr_measures_regr.rae, mlr_measures_regr.rmse, mlr_measures_regr.rrse, mlr_measures_regr.rse, mlr_measures_regr.sae, mlr_measures_regr.smape, mlr_measures_regr.srho, mlr_measures_regr.sse

mlr3 documentation built on Oct. 18, 2024, 5:11 p.m.