mlr3measures (version 0.3.1)

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

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

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