The models were evaluated using the coefficient of determination (R²), which measures the proportion of variance explained by the model, as well as the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to assess the quality of the predictions. These metrics complement R² by providing insights into the magnitude of errors and the model's sensitivity to outliers .