## 4 Results
### Cross-Dataset MultiClass Classification
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **Class** & **Precision** & **Recall** & **F1 Score** & **Accuracy** & **Training Time (s)** & **Prediction Time (s)** \\ \hline \multirow{8}{*}{Decision Tree} & 0 & 0.00 & 0.00 & 0.00 & & & \\  & 1 & 0.39 & 1.00 & 0.56 & & & \\  & 2 & 0.58 & 0.57 & 0.58 & & & \\  & 3 & 0.00 & 0.00 & 0.00 & & & \\  & 4 & 0.00 & 0.00 & 0.00 & 0.428093 & 307.40 & 102.76 \\  & 5 & 0.51 & 0.89 & 0.65 & & & \\  & 6 & 0.00 & 0.00 & 0.00 & & & \\  & 7 & 0.00 & 0.00 & 0.00 & & & \\  & 8 & 0.57 & 0.27 & 0.37 & & & \\ \hline \multirow{8}{*}{Naive Bayes} & 0 & 0.09 & 0.96 & 0.17 & & & \\  & 1 & 0.66 & 0.52 & 0.58 & & & \\  & 2 & 1.00 & 1.00 & 1.00 & & & \\  & 3 & 0.00 & 0.00 & 0.00 & & & \\  & 4 & 0.99 & 0.67 & 0.80 & 0.553320 & 212.16 & 55.24 \\  & 5 & 0.55 & 0.91 & 0.68 & & & \\  & 6 & 0.73 & 0.12 & 0.20 & & & \\  & 7 & 0.51 & 0.89 & 0.65 & & & \\  & 8 & 0.57 & 0.27 & 0.37 & & & \\ \hline \multirow{8}{*}{Logistic Regression} & 0 & 0.31 & 0.89 & 0.46 & & & \\  & 1 & 0.20 & 0.06 & 0.09 & & & \\  & 2 & 0.95 & 1.00 & 0.97 & & & \\  & 3 & 1.00 & 1.00 & 1.00 & & & \\  & 4 & 0.99 & 0.67 & 0.80 & 0.846680 & 567.21 & 268.12 \\  & 5 & 0.69 & 0.91 & 0.78 & & & \\  & 6 & 0.38 & 0.13 & 0.20 & & & \\ \hline \multirow{8}{*}{KNN} & 0 & 0.99 & 0.98 & 0.99 & & & \\  & 1 & 0.97 & 0.83 & 0.89 & & & \\  & 2 & 1.00 & 1.00 & 1.00 & & & \\  & 3 & 1.00 & 1.00 & 1.00 & & & \\  & 4 & 0.96 & 0.99 & 0.98 & 0.972670 & 2182.4 & 1136.56 \\  & 5 & 0.91 & 0.90 & 0.91 & & & \\  & 6 & 0.80 & 0.81 & 0.80 & & & \\  & 7 & 0.97 & 0.83 & 0.89 & & & \\  & 8 & 0.99 & 0.98 & 0.99 & & & \\ \hline \multirow{8}{*}{Gradient Boost} & 0 & 0.58 & 0.57 & 0.58 & & & \\  & 1 & 0.91 & 0.91 & 0.92 & & & \\  & 2 & 1.00 & 1.00 & 1.00 & & & \\  & 3 & 0.72 & 0.45 & 0.56 & & & \\  & 4 & 0.86 & 0.96 & 0.91 & 0.873166 & 1657.5 & 4400.12 \\  & 5 & 1.00 & 1.00 & 1.00 & & & \\  & 6 & 0.62 & 0.64 & 0.63 & & & \\  & 7 & 0.76 & 0.73 & 0.75 & & & \\  & 8 & 1.00 & 1.00 & 1.00 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of multiclass classification of best 10 features. The main motivation for incorporating the best 10 and 20 features from the dataset is the model's improvement in performance due to less influence of noise and irrelevant information and faster training time. Reduced overfitting and more robust system architecture can also be taken as an inspiration for the process. Table 11 provides the classification result utilizing best 20 features from the dataset. In this scenario, the increase in the best features resulted in increased accuracy scores, as expected. Given the relationship between training and prediction times and the number of features, adding more features also resulted in longer training and prediction periods. The decision tree algorithm demonstrated an increase in both accuracy and training and prediction times when the features were added from 10 to 20. The decision tree algorithm performed well, with 20 best features across all classes regarding precision, recall, and F1-score. The results were similar for the random forest classifier, which also showed an increase in evaluation metrics when the features were added from 10 to 20. The prediction times for the KNN classifier were typically higher, while the training times were short and constant. The gradient boost classifier and the ensemble approach using the best