## 4 Experiments
### Machine Learning Models  
Random Forest (RF): The RF model [68] is an ensemble classifier that uses randomly chosen subsets of training data and features to build multiple decision trees. The classifier combines predictions from these individual decision trees to provide accurate and reliable classifications. Furthermore, RF can identify and rank the features that contribute most significantly to distinguishing between target classes, making it a powerful tool for feature importance analysis [69].  
Logistic Regression (LR): This model is a straightforward parametric statistical method that constructs a model by identifying classification parameters capable of distinguishing between groups and formulating associated classification rules through numerical optimization. One of LR's key advantages is its ability to operate without making assumptions about class distributions in the feature space, enhancing its versatility in various classification tasks [2].  
Adaptive Boosting (AdaBoost): AdaBoost [70] is an ensemble learning method that iteratively improves classification performance by focusing on the errors of weak classifiers. Unlike Random Forest, which uses parallel ensembling, AdaBoost employs \"sequential ensembling\", where each classifier is trained to correct the errors made by its predecessor. When it comes to improving decision tree performance for binary classification jobs, AdaBoost is especially useful. Nevertheless, its overall efficacy may be impacted by its sensitivity to outliers and noisy data [71].  
Gradient Boosting (GB): Gradient Boosting (GB) is an ensemble model that builds a final predictive model by sequentially merging several independent models, usually decision trees. The method optimizes weights by leveraging gradients to minimize a specified loss function. By iteratively adding weaker models to correct the errors of the ensemble, Gradient Boosting creates a more robust predictor. This approach often outperforms single strong models in data-driven tasks, as it combines the strengths of weaker models into a cohesive and powerful ensemble estimator [72,73].  
Extreme Gradient Boosting (XGBoost): XGBoost is a refined version of gradient boosting that incorporates more precise approximations to optimize the model. It employs the loss function's second-order gradients to decrease errors and includes sophisticated regularization techniques to reduce overfitting. These features enhance model performance and generalization. XGBoost is a preferred option for difficult data-driven tasks because of its quick learning capabilities and exceptional efficacy in managing huge amounts of data [71].  
Categorical Boosting (CatBoost): CatBoost [74] is a gradient boosting technique designed specifically for handling categorical data effectively. Unlike other popular gradient boosting methods like XGBoost, CatBoost employs ordered boosting, ensuring that eachmodel in the ensemble is trained exclusively on historical data. This approach enhances generalization, and the likelihood of overfitting is decreased, making CatBoost a highly efficient and accurate option for datasets with significant categorical features.