- MAE is expressed in the same units as your target variable, making it easy to interpret. For example, if predicting area in m², an MAE of 5.0 means predictions are off by 5 m² on average. Use MAE to compare different regression models and choose the most accurate one.
- Lower MAE values indicate better prediction accuracy
Use Cases
- Model evaluation: Measure how accurately your regression model predicts continuous values.
- Model comparison: Compare MAE scores across different algorithms (Linear, Random Forest, Gradient Boosting) to select the best one.
- Performance monitoring: Track prediction accuracy as you add more features or data to your training set.