Mean Absolute Error Explained: Measuring Model Accuracy

So you’ve run a model and now you need to evaluate it. Choosing the right model metric can feel confusing since there are options like MAE, MSE, RMSE, and more. 

All these metrics measure error, but they tell slightly different stories about how your model is performing. In this article, we’ll focus on one common one that you need to know as a data scientist: mean absolute error (MAE). As you will see, MAE offers a straightforward view of prediction accuracy without overcomplicating things.

What Is Mean Absolute Error (MAE)?

When you need a clear way to measure how accurate your predictions are, mean absolute error is a good place to start. It tells you, on average, how far off your model’s predictions are from the actual values without worrying about whether these predictions were too high or too low.

In this equation, n is the total number of predictions. The absolute value, which you can see with the vertical bars, makes sure that all the errors are treated as positive, so both over- and under-predictions count the same. It’s a simple, direct measure of your model’s average prediction error, which is why it’s so often used.

Why Should You Care About MAE?

I started to mention all this already: MAE is useful mostly because it’s easy to understand. If your MAE is 5, that means your model’s predictions are off by 5 units on average.

To appreciate this, though, you hae to think about mean squared error, abbreviated as MSE, which has the effect of giving more weight to large errors by squaring them. With MSE, outliers to skew your evaluation too much.

How Do You Calculate MAE? (With Python Example)

Let’s go through a quick example using Python. Suppose you’ve built a model to predict monthly sales, and now you want to check how accurate it was. Here’s how to calculate MAE:

import numpy as np

actual = np.array([100, 150, 200, 250])
predicted = np.array([110, 140, 210, 240])

mae = np.mean(np.abs(actual - predicted))
print("MAE:", mae)

MAE: 10.0

This code finds the absolute difference between each actual and predicted value, then takes the average. The result (10.0 here) is the typical size of the prediction error.

Where Is MAE Used?

Retailers often use MAE to check how closely their forecasts match actual sales. This is a very common use case. A high MAE could mean the model needs to be retrained or adjusted. I could also imagine in healthcare, MAE can measure how close predicted recovery times are to real outcomes. A lower MAE gives more confidence that the model is producing useful estimates.

These are just a couple of examples, but MAE can be used in any situation where you’re comparing predicted and actual numerical values. The possibilities are too many to list!

How Does MAE Compare to Other Metrics?

MAE isn’t the only way to evaluate prediction error. Depending on what kind of errors you care about most, other metrics might be better suited.

MAE vs. MSE

MSE also looks at the difference between predicted and actual values, but it squares each error before averaging. That makes it more sensitive to large errors. If big mistakes matter more in your use case, MSE might be a better fit.

MAE vs. RMSE

RMSE is the square root of MSE. It puts the error back into the original units, which makes it easier to interpret than MSE. But like MSE, it gives more weight to large errors.

If you want to treat all errors the same and keep things simple, MAE is a good choice. But if you’re more concerned about the impact of large mistakes, MSE or RMSE could be more appropriate.

Conclusion

MAE is a clear and practical way to measure how far your predictions are from actual values. It’s easy to calculate, easy to explain, and useful in a wide range of applications.

While it won’t highlight large errors the way MSE or RMSE does, it gives a fair view of overall accuracy. Use it when you want a direct measure that’s not overly influenced by a few large misses.

All this said, you're going to want to use MAE alongside other metrics, especially as you compare models or fine-tune performance. Over time, you’ll get a sense for when MAE is enough and when another metric gives more insight. Take our Understanding Data Science course and our Introduction to Regression with statsmodels in Python course to help develop both your skills and your intuition.