Kappa

Kappa

Mean Absolute Error (MAE)

$$ \text{MAE} = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i| $$

Mean Absolute Error is a metric evaluating the performance of regression models. It measures the average of the absolute differences between the predicted values and the actual (ground truth) values.  It is a clear, interpretable value representing the average magnitude of the prediction errors, without considering the direction of the error.

where:

• \(n\) is the number of observations in the dataset
• \(y_i\) is the actual value for observation \(i\)
• \(\hat{y}_i\) is the predicted value for observation i
• \(|y_i - \hat{y}_i|\) is the absolute difference between the actual and predicted values

• Lower MAE values indicate better model performance, as the average error between the predicted and actual values is smaller. However, it's essential to interpret the MAE value in the context of the specific problem, as the range of acceptable values may vary depending on the domain and the scale of the target variable.
• When comparing models, the one with the lowest MAE is generally considered to have better predictive performance, assuming the same dataset and target variable.
• However, it's a good idea to consider multiple evaluation metrics, such as RMSE (Root Mean Squared Error) and R-squared, to get a comprehensive understanding of a model's performance.