What is Mean Absolute Error (MAE)?

MAE is the average of the absolute differences between actual and predicted values: MAE = (1/n) * sum(|actual_i - predicted_i|). It tells you, on average, how far off your predictions are from reality in the same units as your target variable.

How is MAE different from RMSE?

MAE treats all errors equally. Root Mean Squared Error (RMSE) squares the errors first, so it penalizes large errors more heavily. MAE is more robust to outliers; RMSE is more sensitive to them.

What is a "good" MAE value?

There is no universal threshold — MAE must be interpreted in context of your data scale and domain. An MAE of 5 is excellent if your values range in the thousands, but poor if they range from 0 to 10.

How many values can I enter?

The calculator handles up to 1000 value pairs in the browser with no server required. For larger datasets, consider computing MAE in Python (scikit-learn) or R.

Does the order of values matter?

Yes. Each actual value is paired with the corresponding predicted value by position, so both lists must be in the same order.

Mean Absolute Error Calculator

Name: Mean Absolute Error Calculator
Availability: InStock
Author: Nham Vu

Enter actual and predicted values to instantly calculate Mean Absolute Error (MAE) for evaluating regression model accuracy.

Input Values

Actual Values (one per line or comma-separated)

Predicted Values (same order as actual)

MAE Formula

MAE = (1/n) × ∑ |actual_i − predicted_i|

Where n is the number of observations, and the sum is over all observation pairs.

Enter values and click Calculate to see results

Mean Absolute Error (MAE)

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Min Error

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Max Error

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Pairs

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Per-Pair Breakdown

#	Actual	Predicted	\|Error\|	Magnitude

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Summary

Enter actual and predicted values to instantly calculate Mean Absolute Error (MAE) for evaluating regression model accuracy.

How it works

Enter your actual (observed) values — one number per line or comma-separated.
Enter your predicted (model output) values in the same order.
Click "Calculate MAE" to compute the result.
The tool computes the absolute difference for each pair, then averages them.
Review per-pair breakdown to identify where your model performs poorly.

Use cases

Evaluate regression model performance in machine learning experiments.
Compare multiple models to select the best predictor.
Diagnose which observations have the highest prediction error.
Quickly sanity-check predictions from spreadsheet data.
Teach or demonstrate MAE as a metric in data science courses.
Validate forecast accuracy for time-series or demand-planning models.

Frequently Asked Questions

Last updated: 2026-06-09 · Reviewed by Nham Vu