# MAPE / Mean absolute percentage error

The MAPE measures the mean absolute percentage error in percent. The percentage error is a relative error.

The MAPE over the whole dataset would then be the mean value of all such errors. One weakness with MAPE is that it is not defined when the true value equals zero (because you need to divide by the true value.) Thus, if you have a regression problem where many of the true values are zero, you should probably look for another metric.

The MAPE also tends to be biased in favor of small predictions, because a prediction smaller than the true value can never have an error over 100%, whereas too large predictions can have arbitrarily large error.

## Example of use

If the true value is 50 and your model predicts 55, the absolute relative error for that data point is 10% (deviation from true value / true value = 5/50).
If it had predicted 45, the error would also have been 5/50 = 10%, since MAPE uses the absolute error (no negative values).