mlr3measures (version 1.2.0)

sse: Sum of Squared Errors

Description

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

Usage

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

Value

Performance value as numeric(1).

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.

Meta Information

• Type: "regr"

• Range: \([0, \infty)\)

• Minimize: TRUE

• Required prediction: response

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.

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)