Precisely adjusting hyperparameters is essential for accurate model predictions and optimizing predictive precision. To achieve this, hyperparameter tuning techniques such as GridSearchCV are employed. The optimal parameter obtained in different algorithms for NHOC and density (ρ) using GridSearchCV refer to Table in supplementary. GridSearchCV systematically explores a subset of hyperparameters, evaluating the model's performance through cross-validation of the training data. This approach simplifies the tuning process and improves predictive accuracy and optimized model performance .