sle: Squared Log Error (per observation)

In mlr3measures: Performance Measures for 'mlr3'

Squared Log Error (per observation)

Description

Calculates the per-observation squared error as

\left( \ln (1 + t_i) - \ln (1 + r_i) \right)^2.

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

Note that this is an unaggregated measure, returning the losses per observation.

Usage

sle(truth, response, ...)

Arguments

truth	(numeric()) True (observed) values. Must have the same length as response.
response	(numeric()) Predicted response values. Must have the same length as truth.
...	(any) Additional arguments. Currently ignored.

Value

Performance value as numeric(length(truth)).

Meta Information

• Type: "regr"

• Range (per observation): [0, \infty)

• Minimize (per observation): TRUE

• Required prediction: response

See Also

Other Regression Measures: ae(), ape(), bias(), ktau(), linex(), mae(), mape(), maxae(), maxse(), medae(), medse(), mse(), msle(), pbias(), pinball(), rae(), rmse(), rmsle(), rrse(), rse(), rsq(), sae(), se(), smape(), srho(), sse()

mlr3measures documentation built on Sept. 12, 2024, 7:20 a.m.