Understanding MAE
MAE stands for Mean Absolute Error. It's a common metric used to evaluate the accuracy of a model's predictions. A lower MAE indicates better model performance, as it means the average difference between the model's predictions and the actual values is smaller.
What constitutes a "good" MAE?
There's no single answer to what constitutes a "good" MAE. It depends heavily on the specific context of your problem, including:
- The scale of your data: A MAE of 10 might be excellent if your data is in the thousands, but terrible if it's in the tens.
- The acceptable error tolerance: For some applications, even small errors can be unacceptable.
- The performance of other models: Compare your model's MAE to other models or benchmarks to see how it stacks up.
Examples of Good MAE Values
- Predicting house prices: A MAE of $10,000 might be acceptable for a model predicting house prices in the millions.
- Predicting customer churn: A MAE of 5% might be considered good if it means the model accurately predicts 95% of customer churn.
Practical Insights
- Focus on relative improvement: When comparing models, look for significant improvements in MAE, even if the absolute value is still high.
- Consider other metrics: MAE is just one metric. Look at other metrics like Mean Squared Error (MSE) and R-squared to get a more complete picture of your model's performance.
