## 4 Results and Analysis
### Results of the EEG Emotion Dataset
#### 4.2.2 Results of Embedded-Based Feature Selection on the EEG Emotion Dataset  
Table 4 presents the results of our experiments focused on optimizing machine learning model performance using an embedded feature selection method, LASSO. The baseline model was implemented on the original dataset without feature selection, which contained 2548 features. The models exhibited an accuracy of 98.78% with RF, 96.72% with logistic regression, 99.30% with XGBoost, and 96.67% with SVM. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. of** & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy (\%)**} & \multirow{2}{*}{**Precision (\%)**} & \multirow{2}{*}{**Recall (\%)**} & \multirow{2}{*}{**F1 Score (\%)**} \\
**Features** & & & & & \\ \hline \multirow{5}{*}{2548} & RF & 98.78 & 98.79 & 98.78 & 98.78 \\  & Logistic & 96.72 & 96.78 & 96.71 & 96.70 \\  & Regression & 99.30 & 99.30 & 99.30 & 99.30 \\  & XGBoost & SVM & 96.67 & 96.70 & 96.66 & 96.66 \\ \hline \multirow{5}{*}{100} & RF & **98.82** & **98.82** & **98.80** & **98.80** \\  & Logistic & **96.80** & **96.80** & **96.80** & **96.79** \\  & Regression & 99.20 & 99.20 & 99.20 & 99.20 \\  & SVM & **96.75** & **96.75** & **96.75** & **96.75** \\ \hline \multirow{5}{*}{500} & RF & **98.94** & **98.94** & **98.93** & **98.93** \\  & Logistic & **97.86** & **97.84** & **97.84** & **97.85** \\  & Regression & **99.33** & 99.33 & **99.31** & **99.32** \\  & XGBoost & 99.33 & 99.33 & 99.31 & **99.32** \\  & SVM & **97.01** & **97.00** & **97.01** & **97.01** \\ \hline \multirow{5}{*}{1000} & RF & **98.91** & **98.90** & **98.91** & **98.90** \\  & Logistic & **97.78** & **97.78** & **97.78** & **97.77** \\  & Regression & **99.33** & **99.33** & **99.33** & **99.32** \\  & XGBoost & SVM & **97.10** & **97.10** & **97.10** \\ \hline \multirow{5}{*}{1500} & RF & **98.86** & **98.85** & **98.54** & **98.54** \\  & Logistic & **97.64** & **97.62** & **97.62** & **97.62** \\ \cline{1-1}  & Regression & 98.57 & 98.55 & 98.55 & 98.57 \\ \cline{1-1}  & SVM & **96.80** & **96.78** & **96.78** & **96.76** \\ \hline \multirow{5}{*}{2000} & RF & **98.84** & **98.83** & **98.84** & **98.84** \\ \cline{1-1}  & Logistic & **97.12** & **97.05** & **97.06** & **97.05** \\ \cline{1-1}  & XGBoost & 98.54 & 98.53 & 98.53 & 98.53 \\ \cline{1-1}  & SVM & **96.78** & **96.75** & **96.75** & **96.77** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of machine learning after applying the filter-based feature selection method. Improved results compared to models without feature selection are marked in boldface. After applying feature selection using LASSO, the number of features was reduced to 95. This reduction aimed to simplify the model and potentially improve the performance without sacrificing accuracy. The results revealed interesting shifts in model metrics. RF showed an increase in accuracy to 99.39%, with precision, recall, and F1 score also rising to 99.39%. This improvement suggests that the reduced feature set not only maintained but also enhanced performance, likely due to the elimination of less informative features. Logistic regression also benefited from LASSO feature selection, with an accuracy increase to 97.70% and consistent improvements across precision, recall, and F1 score, each reaching 97.70%. Similarly, SVM showed marked improvements, achieving an accuracy of 97.80% and uniform increases in precision, recall, and F1 score to 97.80%. XGBoost continued to perform exceptionally well even after feature selection, with an accuracy of 99.34% and similar increases in precision, recall, and F1 score to 99.35%. These results indicate that XGBoost was resilient to feature reduction, maintaining high performance. In conclusion, the application of LASSO regularization demonstrated its effectiveness in selecting a streamlined set of informative features, which either maintained or improved the performance of machine learning models.