Mean Absolute Error (MAE)
Learn about Mean Absolute Error (MAE), a popular metric for evaluating the accuracy of regression models. Understand how it measures prediction errors.
Mean Absolute Error (MAE)
Mean Absolute Error (MAE) is a metric used to evaluate the performance of a regression model. It measures the average absolute difference between the actual and predicted values in a dataset. The lower the MAE, the better the model's performance.
Formula for MAE
The formula for calculating Mean Absolute Error is:
MAE = Σ(|actual - predicted|) / n
Where:
- MAE: Mean Absolute Error
- Σ: Summation symbol
- |actual - predicted|: Absolute difference between actual and predicted values
- n: Number of data points
Interpretation of MAE
MAE is a non-negative value, where lower values indicate better model performance. A MAE of 0 indicates a perfect model that predicts the actual values with no error. However, in real-world scenarios, achieving a MAE of 0 is rare. It is important to compare the MAE of different models to determine which one performs better in terms of accuracy.
Example Calculation of MAE
Let's consider a simple example to calculate Mean Absolute Error:
| Actual Value | Predicted Value | Absolute Difference |
|---|---|---|
| 10 | 8 | 2 |
| 20 | 18 | 2 |
| 15 | 12 | 3 |
Using the formula for MAE:
MAE = (|10-8| + |20-18| + |15-12|) / 3
MAE = (2 + 2 + 3) / 3
MAE = 7 / 3
MAE = 2.33
Importance of MAE
Mean Absolute Error is a simple and intuitive metric that provides a clear understanding of how well a regression model is performing. It is easy to interpret and compare across different models. MAE is less sensitive to outliers compared to other metrics like Mean Squared Error (MSE), making it a preferred choice in certain scenarios.
Conclusion
Mean Absolute Error (MAE) is a useful metric for evaluating the accuracy of regression models. It quantifies the average absolute difference between actual and predicted values, providing a measure of model performance. Lower MAE values indicate better accuracy, with 0 representing a perfect prediction. By calculating and comparing MAE values, data scientists and analysts can assess the effectiveness of different regression models and make informed decisions based on their performance.
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