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:

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.