Small errors
pairs = 3, 2.5 -0.5, 0 2, 2 7, 8
Result: MAE 0.5
MAE Calculator
Mean absolute error (MAE) is the average of the absolute differences between actual and predicted values. Unlike MSE it does not square errors, so outliers do not dominate — it reflects the typical magnitude of the error. Paste one "actual, predicted" pair per line and get MAE, plus RMSE, MSE, and the pair count.
Enter your values and press Calculate — the result appears here.
Worked examples
Perfect predictions
pairs = 10, 10 20, 20 30, 30
Result: MAE 0
The outlier does not dominate
pairs = 1, 1 2, 2 3, 10
Result: MAE 2.33 (vs MSE 16.67)
Frequently asked questions
What does MAE measure?
The average absolute difference between predictions and reality. An MAE of 0.5 means predictions are typically off by half a unit. It is in the same units as the target, so it is easy to interpret.
How is MAE different from MSE?
MAE averages absolute errors; MSE averages squared errors. Squaring gives MSE a much stronger reaction to large errors — one bad prediction can dominate MSE while barely moving MAE.
When should I prefer MAE?
When you want a robust, typical-error view and outliers should not dominate the score — for example demand forecasting or pricing, where a few extreme values are noise rather than signal.
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