mse: Mean Squared Error

In mlr3measures: Performance Measures for 'mlr3'

Mean Squared Error

Description

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.

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.

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(), 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)

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