sse: Sum of Squared Errors

In mlr3measures: Performance Measures for 'mlr3'

Sum of Squared Errors

Description

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

Usage

sse(truth, response, sample_weights = NULL, ...)

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.
sample_weights	(numeric()) Vector of non-negative and finite sample weights. Must have the same length as truth. Weights for this function are not normalized. Defaults to sample weights 1.
...	(any) Additional arguments. Currently ignored.

Details

The Sum of Squared Errors is defined as

\sum_{i=1}^n w_i \left( t_i - r_i \right)^2.

where w_i are unnormalized weights for each observation x_i, defaulting to 1.

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(), maxse(), medae(), medse(), mse(), msle(), pbias(), pinball(), rae(), rmse(), rmsle(), rrse(), rse(), rsq(), sae(), se(), sle(), smape(), srho()

Examples

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

mlr3measures documentation built on April 17, 2026, 5:06 p.m.