Measure to compare true observed response with predicted response in regression tasks.
Arguments
- truth
(
numeric()
)
True (observed) values. Must have the same length asresponse
.- response
(
numeric()
)
Predicted response values. Must have the same length astruth
.- sample_weights
(
numeric()
)
Vector of non-negative and finite sample weights. Must have the same length astruth
. 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 isNaN
.- ...
(
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 \(0\).
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 .