# Deciphering Autonomous Vehicle Regulations with Machine Learning
## 3 Methodology
### Machine Learning
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Theory of Operations** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{DT} & A flowchart-like structure in which each internal node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label. & Easy to interpret and visualize. Can handle both numerical and categorical data and requires little data preparation. & Prone to overfitting, especially with noisy or complex datasets. Can create biased trees if some classes dominate. \\ \hline \multirow{4}{*}{GB} & Builds an additive model in a forward stage-wise fashion, allowing for the optimization of arbitrary differentiable loss functions. & Highly effective on various problems. Can handle mixed types of data, and often provides high predictive accuracy. & Requires careful tuning, is computationally expensive, and can overfit on small datasets. \\ \hline \multirow{4}{*}{XGB} & Builds an additive model in a forward stage-wise fashion, allowing for the optimization of arbitrary differentiable loss functions. & Hindles sparse data well, provides built-in cross-validation, works well on large and complex datasets, and often outperforms other algorithms in predictive accuracy. & Computationally intensive, requires careful tuning of parameters, and may overfit if not properly regularized. \\ \hline \multirow{4}{*}{XGB} & Short for adaptive boosting, is an ensemble boosting technique that adjusts the weights of weak classifiers to form a strong classifier. & Often achieves high accuracy, is less prone to overfitting, and improves the accuracy of weak classifiers. & Sensitive to noisy data and outliers, and performance can suffer if weak classifiers are too complex. \\ \hline \multirow{4}{*}{RF} & An ensemble learning method that constructs multiple decision trees during training and outputs the mode of classes of the individual trees. & Effective in high-dimensional spaces, robust to overfitting, and handles both categorical and numerical data well. & Model interpretability can be challenging, and performance is dependent on the strength of individual trees. \\ \hline \multirow{4}{*}{NB} & A simple probabilistic classifier based on applying [PERSON] theorem with strong (naive) independence assumptions between the features. & Simple, fast, and efficient with large datasets. Performs well with categorical input variables compared to numerical variables. & The assumption of independent predictors is rarely true. Has a notable limitation with data scarcity for a category of a categorical variable. \\ \hline \multirow{4}{*}{kNN} & A non-parametric method that classifies a sample by a plurality vote of its neighbors, assigning the class most common among its \(k\) nearest neighbors. & Simple, intuitive, and effective in practice, especially for low-dimensional datasets. & Computationally expensive, especially as dataset size grows. Sensitive to irrelevant or redundant features. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Machine learning models applied.