## 4 Machine Learning (ML)
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**ML Model** & **Advantages** & **Disadvantages** \\ \hline \hline \multirow{8}{*}{Naive Bayes} & \(\bullet\) & Time-efficient. & \(\bullet\) & Limited applicability in real-world use cases due to its assumption that all predictors/features are independent, which rarely happens in real life. \\  & \(\bullet\) & Suitable for solving multi-class prediction problems & \(\bullet\) & Smoothing technique is needed to solve the \\  & \(\bullet\) & Better performance and requires fewer training data than other models if its assumption of the independence of features holds true. & \(\bullet\) & 'zero-frequency problem’ where it assigns zero probability to a categorical variable whose category in the test dataset was unavailable in the training dataset. \\  & \(\bullet\) & Better suited for categorical input variables than numerical variables. & \(\bullet\) & Its probability output estimations are not precise in some cases. \\ \hline \multirow{8}{*}{Decision Tree} & \(\bullet\) & Easy to use and understand. & \(\bullet\) & Prone to overfitting. \\  & \(\bullet\) & Can handle both categorical and numerical data. & \(\bullet\) & Require some kind of measurement as to how well they are doing. \\  & \(\bullet\) & Resistant to outliers, hence, require little data & \(\bullet\) & Need to be careful with parameter tuning. \\  & \(\bullet\) & Pre-processing. & \(\bullet\) & Can create biased learned trees if some classes \\  & \(\bullet\) & New features can be easily added. & \(\bullet\) & Need to be careful with parameter tuning. \\  & \(\bullet\) & Can be used to build larger classifiers by using ensemble methods. & \(\bullet\) & Can create biased learned trees if some classes \\ \hline \multirow{8}{*}{Random Tree Forest} & \(\bullet\) & Reduces overfitting in decision trees and helps to improve the accuracy. & \(\bullet\) & It requires large computational power as well as resources as it builds numerous trees to combine their outputs. \\  & \(\bullet\) & It is flexible to both classification and regression problems. & \(\bullet\) & It takes a long time to train because it combines many decision trees to determine the class. \\  & \(\bullet\) & It works well with both categorical and continuous values, and it automates missing values in data. & \(\bullet\) & It also lacks interpretability due to the ensemble of decision trees and fails to determine the \\  & \(\bullet\) & Normalizing of data is not required as it uses a rule-based approach. & \(\bullet\) &'s significance of each variable. \\ \hline \multirow{8}{*}{Gradient Boosted Tree} & \(\bullet\) & Is generally more accurate compared to other models. & \(\bullet\) & Prone to overfitting. \\  & \(\bullet\) & It trains faster especially on larger datasets. & \(\bullet\) & It can be computationally expensive and take a long time to train, especially on central processing units (CPUs). \\  & \(\bullet\) & It provides support for handling categorical features. & \(\bullet\) & It can be hard to interpret the final models. \\  & \(\bullet\) & Able to handle missing values natively. & \(\bullet\) &'s' Accuracy depends on the quality of the data. \\ \hline \multirow{8}{*}{k-Nearest Neighbor} & \(\bullet\) & Quick calculation time. & \(\bullet\) & Accuracy depends on the quality of the data. \\  & \(\bullet\) & Simple algorithm. & \(\bullet\) & With large data, the prediction stage might be slow. \\ \cline{1-1}  & \(\bullet\) & Versatile and useful for regression and classification. & \(\bullet\) & Sensitive to the scale of the data and irrelevant features. \\ \cline{1-1}  & \(\bullet\) & High accuracy. & \(\bullet\) & Require high memory, needs to store all of the training data. \\ \cline{1-1}  & \(\bullet\) & It does not require any data assumptions. As a result, there is no need to make additional assumptions, fine-tune several parameters, or & \(\bullet\) & Given that it stores all of the training, it can be computationally expensive. \\ \cline{1-1}  & & construct a model. This is especially important in the case of non-linear data. & \(\bullet\) &'s' Accuracy depends on the quality of the data. \\ \hline \hline \end{tabular}
\end{table}