## 2 Methodology
### Classification Techniques  
The machine learning process begins by feeding a training set to an algorithm so it can learn to categorize data into a given number of classes or labels. The six ML models are as follows. RF: a tree-based algorithm commonly used as a supervised machine learning technique for regression and classification problems. This model generates several decision trees \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Abbreviation** \\ \hline Pseudorandom Number & PRN \\ Carrier Doppler & DO \\ Carrier Phase Shift & CP \\ Pseudo Range & PD \\ Prompt In-phase Component & PIP \\ Receiver Time & RX \\ Prompt Quadrature Component & PQP \\ Time of Week & TOW \\ Prompt Correlator & PC \\ Early Correlator & EC \\ Tracking Carrier Doppler & TCD \\ Carrier to Noise & C/N0 \\ Late Correlator & LC \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of features with their abbreviations. on various samples. Every decision tree in the forest can be applied for a majority vote of the class output. The class with the highest votes becomes the model's predicted class. One of the most important aspects of this classifier is its ability to handle continuous and categorical variables for regression or classification problems [26]. ANN: a supervised neural network consisting of three layers: input, hidden, and output. This neural network uses backpropagation as a learning technique for training, testing, and validating data. This function, the gradient of the loss function, can be computed using the weights of every node, one layer at a time, iterating backward from the last layer to prevent redundant computations in the chain rule. An ANN can employ non-linear activation functions, distinguishing it from linear perceptron. This model is sensitive to feature scaling and is a non-convex function with different random weight initializations [27]. Figure 2 provides a schematic overview of this model. SVM: a technique that can find a hyperplane in \(N\)-dimensional space, where \(N\) is the number of input features that can divide the data points into several classes. It is a complex algorithm that can achieve high ACC while preventing over-fitting. The training instances used for the prediction process are selected using a kernel function. Linear SVM, which employs a linear kernel, is faster than non-linear SVM for multi-class data [28]. GNB: a machine learning technique based on the Bayesian Theorem that classifies data observations into one of the pre-defined sets of classes using the information provided by attribute variables. GNB classification suffers from conditional independence. This classifier assumes that attribute values have a Gaussian distribution given the class label [29]. For example, suppose that attribute \(I\) is continuous with a mean \(\mu_{c,i}\), a variance \(\sigma_{i,c}^{2}\), and belongs to the class label c. The probability of observing the value \(x_{i}\) in attribute \(i\) given the class \(c\) is computed using Equation (3):  
\[p(x_{i}|c)\frac{1}{\sqrt{2\pi\sigma_{i,c}^{2}}}e^{\frac{(x_{i}-x_{i})^{2}}{2 \sigma_{i,c}^{2}}} \tag{3}\]  
LR: another powerful supervised machine learning algorithm employed for binary classification and linear regression. This algorithm works based on a logistic function (LF), defined in Equation (4), to model the probabilities for classification problems with binary outputs. This model takes a linear combination of features for the input variable X and applies a non-linear sigmoidal function as given by [30]:  
\[LF=\frac{1}{1+e^{-x}} \tag{4}\]  
CART: a nonparametric algorithm that can identify a population's mutually exclusive and exhaustive subgroups. These subgroups consist of members that can share similar features. These features can impact the dependent variable of interest. This algorithm is one of the oldest and most basic decision tree algorithms. It generates a multi-level structure that resembles tree branches as a visual output.