Mean Absolute Error
What is Mean Absolute Error?
In the artificial intelligence industry, Mean Absolute Error (MAE) is widely used to evaluate the performance of regression models. It calculates the average absolute differences between predicted values and actual values. By disregarding whether the errors are positive or negative, MAE provides a straightforward measure of prediction accuracy. It is especially useful when you want a clear understanding of how far off predictions are, on average, from the actual outcomes. Lower MAE values indicate better model performance as they signify that the predictions are closer to the actual values. This metric is popular due to its simplicity and ease of interpretation, making it a go-to choice for many practitioners in the AI field.
Mean Absolute Error (MAE) is a metric used to measure the average magnitude of errors in a set of predictions, without considering their direction.
Examples
- A retail company uses a regression model to predict daily sales. After comparing the predicted sales to the actual sales, the company calculates the MAE to understand the average error in their predictions, helping them refine their model for better accuracy.
- In healthcare, a hospital uses AI to forecast patient admission rates. By evaluating the MAE, they can determine how well their model predicts the number of daily admissions, allowing them to adjust resources and staffing accordingly.
Additional Information
- MAE is scale-dependent, meaning its value is directly influenced by the scale of the data being analyzed.
- Unlike some other error metrics, MAE does not penalize larger errors more than smaller ones; all errors are treated equally in magnitude.