Mean Absolute Error

Mean Absolute Error (MAE)

Mean Absolute Error (MAE) is a statistical measure used to assess the accuracy of a model’s predictions. It quantifies the average absolute difference between the actual values and the values predicted by the model. Unlike some other error metrics, MAE treats all errors equally, regardless of their direction (i.e., whether predictions are above or below the actual values).

To calculate MAE, follow these steps:

1. Subtract the predicted value from the actual value for each observation to find the error.
2. Take the absolute value of each error to avoid negative differences.
3. Sum all the absolute errors.
4. Divide the total by the number of observations.

The formula for MAE can be expressed mathematically as:

MAE = (1/n) * Σ |Actual_i – Predicted_i|

where n is the number of observations, Actual_i is the actual value, and Predicted_i is the predicted value.

MAE is a widely used metric in fields such as machine learning and forecasting because it is easy to understand and interpret. A lower MAE value indicates a better fit of the model to the data, meaning the predictions are closer to the actual values. However, it is important to note that MAE does not provide information about the direction of errors (whether predictions are overestimates or underestimates), which may be relevant in certain applications.