mape: Mean Absolute Percent Error

In mlr3measures: Performance Measures for 'mlr3'

Mean Absolute Percent Error

Description

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

Usage

mape(truth, response, sample_weights = NULL, na_value = NaN, ...)

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.
na_value	(numeric(1)) Value that should be returned if the measure is not defined for the input (as described in the note). Default is NaN.
...	(any) Additional arguments. Currently ignored.

Details

The Mean Absolute Percent Error is defined as

\frac{1}{n} \sum_{i=1}^n w_i \left| \frac{ t_i - r_i}{t_i} \right|,

where w_i are normalized sample weights.

This measure is undefined if any element of t is 0.

Value

Performance value as numeric(1).

Meta Information

• Type: "regr"

• Range: [0, \infty)

• Minimize: TRUE

• Required prediction: response

References

de Myttenaere, Arnaud, Golden, Boris, Le Grand, Bénédicte, Rossi, Fabrice (2016). “Mean Absolute Percentage Error for regression models.” Neurocomputing, 192, 38-48. ISSN 0925-2312, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.neucom.2015.12.114")}.

See Also

Other Regression Measures: ae(), ape(), bias(), ktau(), linex(), mae(), maxae(), maxse(), medae(), medse(), mse(), 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)
mape(truth, response)

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