Mean Absolute Error (MAE) Calculator
The MAE calculator determines the mean absolute error (MAE) of a regression model based on the actual and predicted values.
How to Use the MAE Calculator?
- First, enter the set of actual and predicted values. Make sure the entered numbers should be comma or space separated. Example: 0.5,1,23 or 0.5 1 23.
- Also, the number of observations must be the same for both lists.
- Press the 'Calculate' button.
- Finally, the tool returns the mean absolute error (MAE) for a model.
What is Mean Absolute Error?
The Mean Absolute Error (MAE) is a metric used to determine the accuracy of predictions in a model. It defines how far the predictions are from the actual values in a regression, on average.
How to Calculate MAE?
The mean absolute error (MAE) is calculated using the following formula:
Where,
Σ = Symbol define 'sum',
Ai = Actual (observed) values,
Pi = Predicted values,
n = Number of observations (data points).
Let's take an example.
Example:
Suppose, the actual (true) values are [6, 8, 7, 12] and the predicted values are [0.6, 4.8, -5, 9]. Compute the MAE for a given model.
Solution:
Here,
Ai = [6, 8, 7, 12]
Pi = [0.6, 4.8, -5, 9]
Now apply the formula.
| = |
| |(6-0.6) + (8-4.8) + (7-(-5)) + (12-9)| | | | 4 |
|
MAE = 5.9