maxse: Max Squared Error

In mlr3measures: Performance Measures for 'mlr3'

Max Squared Error

Description

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

Usage

maxse(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.

Details

The Max Squared Error is defined as

\max \left( t_i - r_i \right)^2.

Value

Performance value as numeric(1).

Meta Information

• Type: "regr"

• Range: [0, \infty)

• Minimize: TRUE

• Required prediction: response

See Also

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

Examples

set.seed(1)
truth = 1:10
response = truth + rnorm(10)
maxse(truth, response)

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