Mean Squared Error — mse • mlr3measures

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

Usage

mse(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. The vector gets automatically normalized to sum to one. Defaults to equal sample weights.
...: (any)
Additional arguments. Currently ignored.

Value

Performance value as numeric(1).

Details

The Mean Squared Error is defined as $$ \frac{1}{n} \sum_{i=1}^n w_i \left( t_i - r_i \right)^2, $$ where \(w_i\) are normalized sample weights.

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(), 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)
mse(truth, response)
#> [1] 0.5658606