Squared Log Error (per observation) — sle • mlr3measures

Calculates the per-observation squared error as ${(\ln (1 + t_{i}) - \ln (1 + r_{i}))}^{2} .$

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()