What is Mean Absolute Error (MAE)?

Mean Absolute Error (MAE) measures the average absolute difference between a regression model's predictions and the actual target values.

It answers a simple question:

On average, how far are the predictions from the true values?

Formula

MAE = (1 / n) × Σ|yi - ŷi|

where:

• yi is the actual value
• ŷi is the predicted value
• n is the number of observations

Simple example

Suppose the actual values are:

10, 20, 30

and the predictions are:

12, 18, 25

The absolute errors are:

|10 - 12| = 2
|20 - 18| = 2
|30 - 25| = 5

So:

MAE = (2 + 2 + 5) / 3 = 3

This means the model's predictions are off by 3 units on average.

Why use absolute values?

Prediction errors can be positive or negative.

If we averaged the raw errors directly, overpredictions and underpredictions could cancel each other out.

Taking the absolute value prevents that cancellation.

MAE is easy to interpret

MAE is expressed in the same unit as the target variable.

For example:

• house-price MAE of ₹50,000 means predictions differ from actual prices by about ₹50,000 on average
• temperature MAE of 2°C means predictions are off by about 2°C on average

How does MAE treat large errors?

Every error contributes in direct proportion to its size.

An error of 10 contributes twice as much as an error of 5.

Because errors are not squared, MAE is generally less dominated by very large errors than squared-error metrics such as MSE.

Is lower MAE better?

Yes.

MAE = 0

means every prediction is exactly correct.

In general, a lower MAE indicates smaller average prediction errors, but whether a particular value is good depends on the scale and business context of the target.

In short, MAE measures the average absolute prediction error in the same units as the target, making it one of the easiest regression metrics to interpret.