mlr3measures (version 0.3.0)

mape: Mean Absolute Percent Error

Description

Regression measure defined as $$ \frac{1}{n} \sum_{i=1}^n \left| \frac{ t_i - r_i}{t_i} \right|. $$

Usage

mape(truth, response, 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.

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.

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, B<U+00E9>n<U+00E9>dicte, Rossi, Fabrice (2016). “Mean Absolute Percentage Error for regression models.” Neurocomputing, 192, 38-48. ISSN 0925-2312, 10.1016/j.neucom.2015.12.114.

See Also

Other Regression Measures: bias(), ktau(), mae(), maxae(), maxse(), medae(), medse(), mse(), msle(), pbias(), rae(), rmse(), rmsle(), rrse(), rse(), rsq(), sae(), smape(), srho(), sse()

Examples

# NOT RUN {
set.seed(1)
truth = 1:10
response = truth + rnorm(10)
mape(truth, response)
# }