## 4 Results
In this experiment, the model was initially tuned with PolyKernel, and the predicted results revealed an accuracy of 61.4% while employing an information gain-based feature selection method. The model was again tuned with different feature selection methods, and this process was repeated multiple times until the highest accuracy value was achieved. The highest accuracy of 68.5% with RBF Kernel was achieved while employing the Relief-F based feature selection method. Complete results for the SVM classifier with various feature selection methods are presented in Table 5. After the SVM model, the random forest was utilised to obtain more effective results. Several machine learning models such as random forest and ANN are non-deterministic, requiring a random seed argument for reproducible results. Random seed denotes the random initial value for the algorithms. We used different seed values to perform the experiments. In random forest, multiple trees were built with seeds that made a forest because the similar nature of trees decreased model performance. So, to achieve better performance, individual trees were built differently. The randomness in the generation of trees could be achieved with the use of random seeds. In this experiment, the model was initially tuned with 50 trees with random seed =7, and the predicted results revealed an accuracy of 68.5%. In contrast, the information gain-based feature selection method was employed. The model was again tuned with different combinations of values, and this process was repeated multiple times until the highest accuracy value was achieved. The highest accuracy of 71.3% with an ROC of 0.65 was obtained when the number of trees was set to 100, and the random seed was set to 8 while using the relief-F-based feature selection method. Complete results for the RF classifier with various feature selection methods are presented in Table 6. After the random forest, the rotation forest was utilised to obtain more effective results. In the rotation forest, ensemble trees were built because the similar nature of trees decreased the model performance. So, to achieve better performance, individual trees were constructed differently. In this experiment, the model was initially tuned with an ensemble size of 5, and the predicted results revealed an accuracy of 65.2%. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Feature** & \multirow{2}{*}{**Confidence**} & \multirow{2}{*}{**
\begin{tabular}{} \end{tabular}**} & **Minimum Number of** & \multirow{2}{*}{**Accuracy (\%)**} & **Precision** & \multirow{2}{*}{**Recall (\%)**} & **ROC** & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**Low**} & **Medium** & **High** & **Weighted Average** \\ \cline{4-11}
**Method** & & & & & & & & & & & \\ \hline Information & 0.05 & 2 & 65.0 & 0.53 & 0.65 & 0.51 & 0.42 & 0.23 & 0.67 & 0.35 & 0.56 \\ Gain & 0.25 & 2 & 62.9 & 0.61 & 0.62 & 0.60 & 0.47 & 0.26 & 0.76 & 0.39 & 0.62 \\
**0.50** & **6** & **67.1** & **0.64** & **0.67** & **0.63** & **0.41** & **0.28** & **0.79** & **0.42** & **0.65** \\ \hline Wrapper & **0.05** & **2** & **62.7** & **0.62** & **1.00** & **0.50** & **0.43** & **0.25** & **0.88** & **0.41** & **0.77** \\ Method & 0.25 & 2 & 60.4 & 0.56 & 0.60 & 0.47 & 0.50 & 0.18 & 0.69 & 0.34 & 0.56 \\  & 0.50 & 6 & 61.7 & 0.55 & 0.61 & 0.61 & 0.46 & 0.19 & 0.68 & 0.33 & 0.57 \\ \hline  & 0.05 & 2 & 65.7 & 0.58 & 0.65 & 0.57 & 0.43 & 0.11 & 0.81 & 0.27 & 0.61 \\ Relief-F & 0.25 & 2 & 63.6 & 0.60 & 0.63 & 0.58 & 0.45 & 0.22 & 0.79 & 0.27 & 0.61 \\  & **0.50** & **6** & **65.5** & **0.68** & **0.68** & **0.63** & **0.40** & **0.35** & **0.80** & **0.50** & **0.68** \\ \hline  & 0.05 & 2 & 55.4 & 0.51 & 0.55 & 0.50 & 0.39 & 0.23 & 0.62 & 0.31 & 0.52 \\ LASSO & **0.25** & **2** & **59.2** & **0.53** & **0.59** & **0.52** & **0.41** & **0.18** & **0.66** & **0.33** & **0.57** \\  & 0.50 & 6 & 51.7 & 0.47 & 0.51 & 0.46 & 0.35 & 0.26 & 0.59 & 0.28 & 0.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of J84 classifier through various feature selection techniques.