## 4 Discussion  
The remaining feature in each scenario then tested for classification purposes using Machine Learning algorithms. Figures 3 and 4 show the performance of machine learning algorithms on various number of selected features on the first scenario using Power Predictive score (PPS) and Recursive Feature Elimination (RFE). Random forest (RF) consistently achieves the best performance of each features subset on both dataset (blue line). Therefore, the rest of the paper evaluated on random forest algorithms only. Our finding identify RF is the best performing algorithms as reported in [8; 9; 36]. Therefore, the rest of the experiment was carried out on random forest (RF). \begin{table}
\begin{tabular}{c c} \hline \hline
**Removed Features** & **Effect on the Output Variable** \\ \hline PathLevel & 0.2295 \\ SubdomainLevel & 0.0431 \\ NumNumericChars & 0.0191 \\ PctExtResourceUrls & \(-\)0.0219 \\ UrlLength & \(-\)0.0745 \\ PathLength & \(-\)0.0761 \\ QueryLength & \(-\)0.0805 \\ NumAmpersand & \(-\)0.0814 \\ NumQueryComponents & \(-\)0.1474 \\ NumDash & \(-\)0.3722 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: List of removed features using Spearman and their effect on the output variable on dataset 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Variable 1** & **Variable 2** & **Spearman Value** \\ \hline
14 & google\_index & status & 0.731171 \\
15 & nb\_slash & length\_words\_raw & 0.720081 \\... &... &... &... \\
2511 & page\_rank & status & \(-\)0.546889 \\
2512 & nb\_hyperlinks & status & \(-\)0.551603 \\
2513 & length\_words\_raw & shortest\_words\_raw & \(-\)0.621968 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: _Cont._Table 15 shows the accuracy and execution time comparison of the selected features of each scenario for dataset 1. In light of the analysis of the accuracy value comparison in the graph in Figure 5, we can see that the accuracy using the Random Forest algorithm the accuracy value is maintained even with the reduction of features using three existing methods. The accuracies on the full features are achieved at 98.1%, and it declines insignificantly from 97% to 96% on 38, 30, and 20 features to 10 features. This data is in line with the hypothesis that reducing low-impact features leads to simpler computation with an insignificant decline in the web phishing dataset recognition rate. Table 16 shows the accuracy and execution time comparison of the selected features of each scenario for dataset 2. In light of the analysis of the accuracy value comparison in \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Features Subset**} & \multicolumn{3}{c}{**Accuracy**} & \multicolumn{3}{c}{**Execution Time**} \\  & **PPS + RFE** & **MICe + RFE** & **Spearman + RFE** & **PPS + RFE** & **MICe + RFE** & **Spearman + RFE** \\ \hline
48 (full) & 98.1\% & 98.1\% & 98.1\% & 0.983028 & 0.983028 & 0.983028 \\ \hline
38 (correlation only) & 98.16\% & 97.86\% & 97.86\% & 0.983681 & 0.978557 & 0.977316 \\ \hline
30 (correlation + RFE) & 97.96\% & 97.53\% & 97.83\% & 0.981095 & 0.972850 & 0.977302 \\ \hline
20 (correlation + RFE) & 97.63\% & 97.53\% & 97.6\% & 0.977835 & 0.974691 & 0.975958 \\ \hline
10 (correlation + RFE) & 96.96\% & 97.06\% & 96.63\% & 0.970703 & 0.969539 & 0.969281 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Feature subset accuracy and execution time on Dataset 1. Figure 4: Comparison of various machine learning algorithms for dataset 2. Figure 3: Comparison of various machine learning algorithms for dataset 1.  
the graph in Figure 6, we can see that the accuracy using the Random Forest algorithm the accuracy value is maintained even with the reduction of features using three existing methods. The accuracies on the full features are achieved at 96.76%, and it declines insignificantly from 96% to 95% on 77, 60, 50, 40, 30, and 20 features to 10 features.