## 4 Results and Analysis
### Results of Classifiers with Hyperparameter Optimization
#### 4.2.6 Hyperparameter Optimization of RTF  
For the Rotation Forest ensemble, along with the base classifier and number of iterations, the percentage of instances to be removed (\(P\)) is also tuned. The results of Rotation Forest hyperparameter optimization are postulated in Table 13. \begin{table}
\begin{tabular}{c c c c} \hline
**Training No.** & **Base Classifier** & **Number of Iterations, I** & **Accuracy** \\ \hline
1. & Decision Stump & 10 & 87.2600 \\ \hline
2. & Decision Stump & 20 & 90.0524 \\ \hline
3. & Decision Stump & 30 & 90.9250 \\ \hline
4. & J48 & 10 & 97.2077 \\ \hline
5. & J48 & 20 & 97.0332 \\ \hline
6. & J48 & 30 & 97.3822 \\ \hline \end{tabular}
\end{table} 
Table 11: Prediction accuracy of Adaboost M1 hyperparameter optimization. \begin{table}
\begin{tabular}{c c c c} \hline
**Training No.** & **Base Classifier** & **Number of Iterations, I** & **Accuracy** \\ \hline
1. & REPTree & 10 & 91.9721 \\ \hline
2. & REPTree & 20 & 91.6230 \\ \hline
3. & REPTree & 30 & 92.6702 \\ \hline
4. & J48 & 10 & 94.2408 \\ \hline
5. & J48 & 20 & 94.7644 \\ \hline
6. & J48 & 30 & 94.4154 \\ \hline \end{tabular}
\end{table} 
Table 12: Prediction accuracy of bagging hyperparameter optimization. From Table 13, Rotation Forest has been trained 12 times by tuning the base classifiers (J48 and Random Forest), iterations (10, 20, and 30), and removed percentage (50 and 40). The base classifier Random Forest provided a betterment in accuracy by about 2% compared to that offered by J48. The utmost accuracy of 97.9058% is attained at I = 10 and \(P\) = 40 at the 10 th training. The accuracy comparison of machine learning models without and with optimization on selected principal components from the CFS method is provided in Table 14.  
From Table 14, the accuracy of machine learning models without optimization varied between 84% and 96%, while with optimization, the accuracy varied between 93% and 98%. The variation in accuracy has dropped from about 12% to 5% with optimization. The bar plot of the accuracy comparison with and without optimization is shown in Figure 8. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training No.** & **Base Classifier** & **Number of Iterations, I** & **Removed Percentage, \(P\)** & **Accuracy** \\ \hline
1. & J48 & 10 & 50 & 95.9860 \\ \hline
2. & J48 & 20 & 50 & 95.9860 \\ \hline
3. & J48 & 30 & 50 & 96.3351 \\ \hline
4. & J48 & 10 & 40 & 95.2880 \\ \hline
5. & J48 & 20 & 40 & 95.9860 \\ \hline
6. & J48 & 30 & 40 & 95.9860 \\ \hline
7. & RF & 10 & 50 & 97.7312 \\ \hline
8. & RF & 20 & 50 & 97.5567 \\ \hline
9. & RF & 30 & 50 & 97.7312 \\ \hline
10. & RF & 10 & 40 & 97.9058 \\ \hline
11. & RF & 20 & 40 & 97.7312 \\ \hline
12. & RF & 30 & 40 & 97.7312 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Prediction accuracy of rotation forest hyperparameter optimization. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Classifier** & **Without Optimization** & **With Optimization** & **Improvement** \\ \hline
1. & Support Vector Machines & 84.64 & 93.19 & 8.55 \\ \hline
2. & K-Nearest Neighbors & 95.81 & 97.03 & 1.22 \\ \hline
3. & J48 Decision Tree & 93.89 & 95.46 & 1.57 \\ \hline
4. & AdaBoost M1 & 87.26 & 97.38 & 10.12 \\ \hline
5. & Bagging & 91.97 & 94.76 & 2.79 \\ \hline
6. & Rotation Forest & 95.99 & 97.91 & 1.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Accuracy comparison of machine learning models without and with optimization. It is observed from Figure 8 that hyperparameter optimization has improved the accuracy of all models. The most remarkable improvement has been achieved by AdaBoost M1 with 10%, followed by SVM with 8.55%. The KNN model was the least improved, with an increase of 1.22%. The J48 accuracy has increased by about 1.57%. The accuracy of Bagging and Rotation Forest ensembles has been enhanced by about 2.79% and 1.92%, respectively.