Mean Absolute Percentage Error (MAPE)

#machine-learning #deep-learning #loss-in-ml #interview

Mean Absolute Percentage Error

$M A P E = \frac{1}{N} \sum_{i} \frac{| y_{i} - \hat{y_{i}} |}{y_{i}} * 100$

Pros of MAPE

Errors are normalized on a common scale; hence easy to understand
The problem of negative and positive cancelling each other is not there

Cons of MAPE

Need to avoid values with ground truth 0 because of $y_{i}$ in denominator
Penalizes negative errors more than positive
Change on actual value, change the error even the error value is same

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