## 3 Materials and Methods
### Methodology
#### 3.2.2 Stage II: Classification
Prediction speed is also fast for binary classification but sluggish for multiclass classification [17]. In this experiment, we tuned the hyperparameters of the SVM for the betterment of the classification results. To obtain the optimal results, we have considered the \"cubic Kernel\" with an \"automatic\" kernel scaling, keeping the box constraint level as 1, and considering the multiclass method as one vs one. **Gaussian support vector machine** (G-SVM) is another prominent Kernel approach used in Support Vector Machine models, using the Gaussian RBF (Radial Basis Function). The RBF kernel is a function whose value is proportional to the distance between the origin and some point [17]. We tuned the hyperparameters of the SVM for the betterment of the classification results. To obtain the optimal results, we have considered the \"Gaussian Kernel\" with an \"automatic\" kernel scaling, keeping the box constraint level as 1, and considering the multiclass method as one vs one. **K-nearest neighbor** (k-NN) is a variant of k-nearest neighbors. The selection of the hyperparameter k is one of several factors that influence the performance of the KNN algorithm. If k is too little, the algorithm becomes more susceptible to outliers [18]. In this experiment, to optimize the algorithm, we tuned the k-NN model with a hyperparameter tuning, empirically. In this study, we used the Cosine variant of the k-NN classifier, with the number of neighbors as 10, considering the distance weight as \"equal\" and the distance metric as \"cosine\". **Boosted trees ensemble learner** (BSE) is used to reduce training time errors. Boosting is an ensemble learning strategy that combines a group of weak learners into strong learners. A random sample of data is chosen, fitted with a model, and then trained sequentially--that is, each model attempts to compensate for the shortcomings of its predecessor [19; 20]. This study attempts to employ the ensemble learner to check the variability of the results for the selected set of features. We also tuned the hyperparameters of the BSE learner during the training phase of the model. Here, we considered the ensemble method as \"AdaBoost\", learner type as \"decision tree\", the maximum number of allowed splits as 20, the number of learners as 30, and the rate of learning as 0.1. **Bagged trees ensemble learner** (BSE) is an acronym for Bootstrap Aggregation; it is an ensemble method, which is essentially a mechanism for overlaying diverse models, data, and methods. [20]. Here, in this study, for the hyperparameter tuning of the BSE, we set the following hyperparameter for the optimal result condition. Here, we used the ensemble type as \"Bag\", learner type as \"Decision tree\", and the number of learners as 30. Subspace Discriminant Ensemble learner (SDE): The majority voting rule was utilized to create the subspace discriminant ensemble, which employed the random subspace ensemble approach with 30 linear discriminant learners and two subspace dimensions [21]. Cross-Validation Method  
A statistical technique called cross-validation is used to evaluate the competence of machine learning models. Since it is simple to comprehend, simple to implement, and produces ability estimates that often have a smaller bias than other approaches, it is frequently used in applied machine learning to compare and select a model for a specific predictive modeling issue. The process contains a single parameter, k, that designates how many groups should be created from a given data sample. As a result, the process is frequently referred to as k-fold cross-validation. If k is decided to be a certain value, k in reference to the model may be replaced by that value. This study incorporates the k-fold cross-validation method for training and validating the trained model. Here, we used a 10-fold cross-validation method, where nine folds were taken for training and one fold for testing.