For each fold, the model's performance was assessed using a specific metric: mean absolute error (MAE), root mean squared error (RMSE), and R2 coefficient of determination. The model's performance was then evaluated across the entire dataset by aggregating the performance metrics across all five cross-validation splits. Cross-validation was used for hyperparameter tuning and performance evaluation. In order to find the configuration that maximizes model performance, various sets of hyperparameters were tested throughout the hyperparameter tuning process.