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mlr3measures (version 1.0.0)

se: Squared Error (per observation)

Description

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

se(truth, response, ...)

Value

Performance value as numeric(length(truth)).

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.

Meta Information

  • Type: "regr"

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

  • Minimize (per observation): TRUE

  • Required prediction: response

Details

Calculates the per-observation squared error as $$ \left( t_i - r_i \right)^2. $$

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(), sle(), smape(), srho(), sse()