## 5 Results and Analysis
### Feature Weightage Calculation and Feature Subset Acquisition
Therefore, we can conclude that there is statistically significant independence between the ranked lists. Furthermore, the lists generated by both GBT and DT are relatively in agreement with each other while the lists produced by DT and RF, and as well as RF and GBT show disagreement. In this regard, each scoring function account for important characteristics of the dataset while not diverging too much in their final results. Furthermore, we can also look at different feature subsets obtained from the aforementioned scoring functions as reported in Table 8 and their Jaccard index value [50]. Figure 9 depicts a Venn diagram for all possible logical relations among different feature subsets. It is interesting to note that, although the ranked lists produced by GBT and DT have a higher correlation, whereas, the feature subsets obtained after applying a threshold value to the respective lists have a lower value on the Jaccard index i.e., 0.1000. On the other hand, a higher Jaccard index value is obtained between RF and GBT feature subsets while their ranked lists reported a negative correlation. Based on these observations, we can conclude that both the lists and the subsequent feature subsets obtained from the scoring functions are not redundant. The resulting selected features collected in lists 1, list 2, and list 3 are based on the decision tree, random forest, and gradient boosted trees, respectively. As it can be seen that there are some variations in the selected features, which shows that each scoring function has its own inductive bias while constructing a model as shown in Table 8. A detailed study of the inductive bias of decision tree models is not within the scope of this study. The averaged evaluation results of the aforementioned seven classification models are provided in Table 9. These results reflect the performance of classification models on reduced datasets acquired from each scoring function. In this regard, the decision tree classifier is constructed based on features present in list L1. Likewise, classifiers for random forest and gradient boosted trees are built on L2 and L3, respectively. The overall performance of the random forest and gradient boosted trees increased. Although the decision tree could not improve the accuracy over the full dataset, it slightly improved the sensitivity. As it can be seen that the automatic threshold selection successfully opted for threshold values which resulted in the selection of important features while also keeping the overall cost of the selected \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scoring Function** & **List** & **Selected Features** \\ \hline Decision Tree & L1 & 15, 6, 17, 24, 5 \\ Random Forest & L2 & 20, 19, 18, 6, 15, 3, 22, 9 \\ Gradient Boosted Trees & L3 & 6, 19, 4, 20, 14, 11, 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Selected features by individual scoring functions. Figure 9: Jaccard index for different feature subsets. feature set low. Hence, the feature subsets produced by different scoring functions are both relevant in enhancing the accuracy of the classification models and distinct.