Beginner · Evaluation

Mean Absolute Error (MAE)

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. The average absolute difference between predictions and true values.

Technical Definition

The average absolute difference between predictions and true values.

How it works

MAE = (1/n) Σ |ŷᵢ − yᵢ|. More robust to outliers than MSE because it doesn't square errors. Often easier to communicate to non-technical stakeholders ('our forecast is off by 3 units on average').

Related Concepts

• Loss Function — A mathematical function that measures how far the model's predictions are from the actual values, guiding the learning process.
• Regression — A supervised learning task that predicts a continuous numeric value.
• Mean Squared Error (MSE) — The average squared difference between predictions and true values.