## 2 Materials and Methods
### Canonical Machine Learning Models  
The support vector machine (SVM) is a method from the domain of statistical learning [21]. It is supervised and utilizes a kernel-based approach. The kernel-based learning technique involves an implicit transformation of the input data into a feature space of higher dimensions, which is defined by a kernel function [21]. There are numerous kernel functions that exist in literature [22]. The linear, polynomial, and radial basis function kernels are among the most commonly used. To put it another way, kernel-based learning employs a linear hyperplane as a decision-making function for problems that are nonlinear, and then it applies a transformation back into the nonlinear space [21]. The SVM uses the Lagrange multiplier to calculate the partial differentiation of each feature in order to find the best solution. The objective of the training process is to optimize the margin of the hyperplane, which is defined as the distance between the hyperplane and the nearest support vectors from each class [23]. A broader margin implies that the hyperplane has a higher probability of effectively handling data that it has not encountered before. As a result, the model simplifies the training data to a significant subset known as support vectors, thereby reducing its complexity [21]. The naive Bayes (NB) method, a simple probability classifier, computes a series of probabilities by counting the occurrence and combinations of values within a certain dataset [24]. Taking into account the value of the class variable, the method employs Bayes' theorem to determine the likelihood of a hypothesis based on the provided evidence, operating under the assumption that the input features are conditionally independent when the class label is given [25]. Despite the fact that this assumption of conditional independence is deemed naive due to its infrequent occurrence in real-world applications, the method is typically adept at rapidly learning in a wide range of controlled classification problems [24]. The strength of the naive Bayes classifier lies in its ability to estimate the necessary parameters (such as the means and variances of variables) for classification with only a minimal amount of training data. Due to the assumption of variable independence, it is only necessary to compute the variances of the variables for each class, rather than the entire covariance matrix [24]. K-nearest neighbor (KNN) is a supervised pattern recognition method that is both linear and non-parametric [26]. The core principle of this method is proximity-based: KNN forecasts the label of an unknown instance by identifying 'k' instances that are similar, determined by the computed Euclidean distances from instances in the training set. The classification is then determined by the group to which the majority of the 'k' objects belong, with any ties resolved by considering the sums of the relevant distances. The 'k' parameter plays a significant role in the classification model and is optimized by evaluating the prediction capability at various 'k' values [27]. The method presents a multitude of advantages. One of its primary attributes is its mathematical simplicity, which enables it to potentially yield classification results that may surpass those derived from more complex pattern recognition techniques. Moreover, its efficacy remains unaltered, irrespective of the spatial configuration of the classes [27]. A multilayer perceptron (MLP) is a type of neural network that consists of neuron layers linked by weighted connections [23]. These neural networks are designed to mimic the information-processing methods of the human brain. The multilayer perceptron begins with an input layer, which includes a neuron for each feature, and concludes with an output layer, which has a neuron for each potential class. In between these layers are hidden layers, which contain a varying number of neurons [28]. This classifier is trained using backpropagation techniques.