Evaluation Metric for Regression Models: Mean Absolute Error (MAE)
When building regression models, it is important to have a way to evaluate the performance of the model. One common evaluation metric for regression models is the Mean Absolute Error (MAE).
The Mean Absolute Error measures the average of the absolute differences between the predicted values and the actual values. It is calculated by taking the average of the absolute differences between the predicted and actual values for each data point.
The formula for calculating the Mean Absolute Error is as follows:
MAE = (1/n) * Σ |yi – ŷi|
Where:
MAE = Mean Absolute Error
n = Number of data points
yi = Actual value
ŷi = Predicted value
One of the benefits of using Mean Absolute Error as an evaluation metric for regression models is that it gives equal weight to all errors. This means that large errors do not have a disproportionately large effect on the overall metric, which can be important in certain applications.
However, it is important to note that the Mean Absolute Error does not indicate the direction of the error, meaning that it does not distinguish between overestimations and underestimations. For certain applications, this may be a limitation of using MAE as an evaluation metric.
In conclusion, the Mean Absolute Error is a useful evaluation metric for assessing the performance of regression models. It provides a simple and intuitive measure of the average error in the predictions, and it gives equal weight to all errors. However, it is important to consider the limitations of MAE and to use it in conjunction with other evaluation metrics when assessing the performance of regression models.
