# Using Feature Selection with Machine Learning for Generation of Insurance Insights
## 5 Discussion  
The objective of this article is to highlight the benefit of feature selection for insurance insights and learning-based tasks. We have proposed a framework for selecting the most influential and discarding irrelevant and noisy features.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Per. Of.** & **No. Sel.** & **GFSA** & **LS** & **Specu** & **USFSM** & **All-Features** \\ \hline \multirow{3}{*}{D1} & 0.2 & 4 & 0.6648 & 0.6295 & 0.6325 & 0.6613 & \\ \cline{2-7}  & 0.5 & 8 & 0.6430 & 0.6530 & 0.6715 & **0.6760** & 0.6588 \\ \cline{2-7}  & 0.8 & 14 & 0.6458 & 0.6695 & 0.6563 & 0.6710 & \\ \hline \multirow{3}{*}{D2} & 0.2 & 18 & 0.9378 & **0.9396** & 0.9388 & 0.9385 & \\ \cline{2-7}  & 0.5 & 44 & 0.9352 & 0.9385 & 0.9385 & 0.9380 & 0.9347 \\ \cline{2-7}  & 0.8 & 70 & 0.9343 & 0.9345 & 0.9334 & 0.9384 & \\ \hline \multirow{3}{*}{D3} & 0.2 & 3 & 0.6814 & 0.6828 & 0.6805 & 0.6831 & \\ \cline{2-7}  & 0.5 & 7 & 0.8931 & 0.6934 & 0.6938 & 0.7394 & 0.9166 \\ \cline{2-7}  & 0.8 & 11 & 0.8894 & 0.9135 & **0.9252** & 0.8950 & \\ \hline \multirow{3}{*}{D4} & 0.2 & 3 & 0.7336 & Mem Ex & Mem Ex & Mem Ex & \\ \cline{2-7}  & 0.5 & 7 & 0.7325 & Mem Ex & Mem Ex & Mem Ex & \\ \cline{2-7}  & 0.8 & 11 & **0.7338** & Mem Ex & Mem Ex & Mem Ex & \\ \hline \multirow{3}{*}{D5} & 0.2 & 2 & **0.8030** & **0.8030** & 0.7794 & **0.8030** & \\ \cline{2-7}  & 0.5 & 5 & 0.7779 & 0.7767 & 0.7789 & 0.7789 & \\ \cline{1-1} \cline{2-7}  & 0.8 & 8 & 0.7819 & 0.7817 & 0.7794 & 0.7799 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: KNN Accuracy for insurance datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Per. of.** & **No. Sel.** & **GFSA** & **LS** & **Specu** & **USFSM** & **All-Features** \\ \hline \multirow{3}{*}{D1} & 0.2 & 4 & 0.6035 & 0.5890 & 0.5893 & 0.6020 & \\ \cline{2-7}  & 0.5 & 8 & 0.6448 & 0.6635 & 0.6598 & 0.6610 & 0.6633 \\ \cline{2-7}  & 0.8 & 14 & **0.6710** & 0.6640 & 0.6618 & 0.6660 & \\ \hline \multirow{3}{*}{D2} & 0.2 & 18 & 0.9403 & 0.9403 & 0.9403 & 0.9403 & \\ \cline{2-7}  & 0.5 & 44 & 0.9403 & 0.9403 & 0.9403 & 0.9403 & 0.9403 \\ \cline{2-7}  & 0.8 & 70 & 0.9403 & 0.9403 & 0.9403 & 0.9403 & \\ \hline \multirow{3}{*}{D3} & 0.2 & 3 & 0.6799 & 0.5708 & 0.5708 & 0.5708 & \\ \cline{2-7}  & 0.5 & 7 & 0.6792 & 0.6799 & 0.6800 & 0.6799 & **0.6829** \\ \cline{2-7}  & 0.8 & 11 & 0.6787 & 0.6823 & 0.6792 & 0.6810 & \\ \hline \multirow{3}{*}{D4} & 0.2 & 3 & 0.7601 & Mem Ex & Mem Ex & Mem Ex & \\ \cline{2-7}  & 0.5 & 7 & 0.7601 & Mem Ex & Mem Ex & Mem Ex & \\ \cline{1-1} \cline{2-7}  & 0.8 & 11 & **0.7601** & Mem Ex & Mem Ex & Mem Ex & \\ \hline \multirow{3}{*}{D5} & 0.2 & 2 & 0.8030 & 0.8030 & 0.8030 & 0.8030 & \\ \cline{1-1} \cline{2-7}  & 0.5 & 5 & 0.8030 & 0.8030 & 0.8030 & 0.8030 & \\ \cline{1-1} \cline{2-7}  & 0.8 & 8 & 0.8030 & 0.8030 & 0.8030 & 0.8030 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: SVM accuracy for insurance datasets.  
We applied our framework to identify the most and least influential feature when applying predictive or descriptive analytical algorithms. In order to demonstrate our claim, we have collected and analysed the results of real insurance datasets publicly available in Kaggle [48]. In most cases (all cases except one), there is at least one subset of features which outperforms all features. This indicates that our benchmark insurance datasets contain irrelevant features that degrade the performance of classification and clustering algorithms.