## 2 Materials and Methods
### Experimental Setup  
The dataset discussed previously (Table 1) was used to analyze and compare the performance using five non-parametric ML models. In the first step, we performed data engineering to ensure the quality and integrity of our dataset. Initially, we checked for outliers and missing values to identify and rectify any irregularities or gaps in the data. Furthermore, to facilitate the integration of categorical variables into our machine learning models, we used the one-hot encoding technique [38]. This method transforms categorical variables into binary vectors, with each category represented as a binary feature, thereby enabling numerical representation in our predictive models. In the subsequent phase, we randomly split the dataset into 80% training and 20% test data, following the widely used 80/20 rule. This rule is common practice in prediction modeling, as it provides an equidistributed split between the training and testing sets, ensuring robust model building regardless of the dataset's domain, size, or class distribution [39].  
In the next step, hyperparameter optimization is performed considering the hyperparameters for each ML algorithm described by [40]. In machine learning, hyperparameters are parameters that are set before the learning process begins and control the behavior of the training algorithm. These include settings such as the learning rate, the number of trees in a random forest, and the regularization parameter in support vector machines [41]. The search space refers to the range of possible values that these hyperparameters can take. During model training, various techniques such as grid search or random search are used to explore this search space to identify the optimal hyperparameter settings that result in the best model performance [42; 43; 44]. Table 2 lists the main hyperparameters and their respective search spaces for each of the five different machine learning algorithms used in our study. The random search (RS) hyperparameter optimization technique, which is \"RandomizedSearch CV\" in the SKlearn library, was deployed [45] to optimize the ML algorithms. This method employs k-fold cross-validation during the search process to evaluate different hyperparameter configurations. Therefore, we used 5-fold cross-validation while optimizing the parameters to ensure robustness and reliability in the hyperparameter tuning process. The trained ML algorithms were evaluated with test data to assess the performance of each ML algorithm. This step was repeated 30 times, and the mean values of performance metrics were reported.  
\begin{table}
\begin{tabular}{c c c} \hline
**ML Algorithm (Classifier)** & **Main Hyperparameters** & **Search Space** \\ \hline \multirow{4}{*}{DT} & criterion & “gini”, “entropy” \\ \cline{2-3}  & max\_depth & [5:50] \\ \cline{2-3}  & min\_sample\_split & [2:11] \\ \cline{2-3}  & min\_samples\_leaf & [1:11] \\ \cline{2-3}  & max\_features & [’auto’, ‘sqrt’, ‘log2’, None] \\ \hline k-NN & n\_neighbors & [1:1000] \\ \hline \multirow{2}{*}{SVM} & C & [0.1:50] \\ \cline{2-3}  & kernal & [’linear’, ‘poly’, ‘rbf’, ‘sigmoid’] \\ \hline \end{tabular}
\end{table}
Table 2: Hyperparameters and their search spaces are used for five different machine learning algorithms.