🔬  Mean Absolute Percentage Error (MAPE)

Mean Absolute Percentage Error (MAPE)#

The average absolute error expressed as a percentage of actuals.

Important

✨ AI-generated content. This page was written with the assistance of an AI language model and is provided as a learning aid. Despite careful review, it may still contain mistakes, omissions, or out-of-date information. Whether you are new to the topic, a team lead, or a senior practitioner, treat it as a starting point rather than an authoritative reference: read it critically and independently verify anything you act on (code, commands, figures, and factual claims) against official documentation and primary sources before relying on it.

What it is#

Mean absolute percentage error expresses each error as a percentage of the actual value, averaged:

\[\text{MAPE} = \frac{100\%}{n}\sum_{i=1}^{n}\left|\frac{y_i - \hat{y}_i}{y_i}\right|.\]

This makes it scale-free — comparable across series of wildly different magnitudes.

The pitfalls#

MAPE explodes when actuals are zero or near-zero (the denominator → 0), and it is asymmetric — over-forecasts can incur unbounded percentage error while under-forecasts are capped at 100%, biasing it toward models that under-predict. For intermittent or zero-heavy data, scaled errors like MASE are safer.


Theme: Model Evaluation & Uncertainty  ·  All terminology



See also

Source article Adapted (context, re-expressed) in our own words from: Mean Absolute Percentage Error (MAPE) (insightful-data-lab.com).

Tags: purpose: reference topic: terminology level: intermediate