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

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

This measure is undefined if any element of \(t\) is \(0\).

## 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, doi:10.1016/j.neucom.2015.12.114
.