Mean Absolute Error

Definition of Mean Absolute Error

Mean Absolute Error (MAE) is a measure of the accuracy of predictions made by a model. It is computed by taking the average of the absolute differences between the predicted values and the actual values for each observation.

How is Mean Absolute Error used?

Mean Absolute Error (MAE) is a widely used measure to quantify the difference between predicted and actual values. It measures the average absolute magnitude of errors in a set of predictions, without considering their direction. This means that big positive and negative errors cancel each other out when calculating the MAE, which makes it more suitable for measuring large errors than root mean square error (RMSE). In addition, MAE is less sensitive to outliers and more robust against them compared to RMSE.

To calculate MAE, a prediction error will be calculated for each data point by subtracting its predicted value from its actual value. Then, these errors will be summed up and divided by the total number of data points. The output is then multiplied by 100 to yield a percentage error rate.

MAE is often used in various machine learning models like regression and classification problems, where it’s usually employed as an evaluation metric alongside other metrics like RMSE or accuracy scores. Its greatest advantage is that it’s relatively easy to interpret; however, some researchers argue that it can’t represent the full distribution of observed values due to its inability to take into account extreme errors or outliers. For example, some studies have shown that even if MAE scores are low on average, they could hide large individual errors depending on their type and size. Therefore, it’s important for practitioners to have an understanding of all potential sources of error and use multiple evaluation metrics when assessing model performance.