## 4 Results
### Random Forest Classifier  
[PERSON] et al. [48] showed that random forest along with other MLAs would create an optimum model for classification of significant factors. With that, this study considered the resulting random forest classifier tree as the optimum tree, seen in Table 5. Specifically, the 96% result from gini and best (similar to the basic decision tree) at 90:10 training and testing ratio showed the highest accuracy with consistent output (standard deviation = 0.00), for which, the generated optimum tree is presented in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **60:40** & **70:30** & **80:20** & **90:10** \\ \hline  & \multicolumn{4}{c}{Random} \\ \hline Gini & 53.38 & 53.76 & 56.05 & 55.11 \\ Standard & 2.743 & 3.128 & 3.408 & 4.671 \\ Deviation & 53.85 & 53.24 & 55.99 & 54.27 \\ Standard & 2.849 & 2.907 & 3.970 & 5.250 \\ \hline  & \multicolumn{4}{c}{Best} \\ \hline Gini & 55.79 & 56.00 & 57.00 & 52.00 \\ Standard & 0.412 & 0.000 & 0.000 & 0.000 \\ Deviation & 53.31 & 55.00 & 53.00 & 59.12 \\ Deviation & 0.751 & 0.000 & 0.000 & 0.997 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Decision Tree Summarized Results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **60:40** & **70:30** & **80:20** & **90:10** \\ \hline  & \multicolumn{4}{c}{Random} \\ \hline Gini & 82.12 & 80.04 & 82.64 & 83.54 \\ Standard & 5.523 & 4.679 & 7.156 & 7.446 \\ \hline Entropy & 82.76 & 79.64 & 84.16 & 84.82 \\ Standard & 4.374 & 6.405 & 5.578 & 5.858 \\ Deviation & \multicolumn{4}{c}{Best} \\ \hline Gini & 88.77 & 84.04 & 92.49 & 96.00 \\ Standard & 0.925 & 1.004 & 0.502 & 0.000 \\ \hline Entropy & 83.39 & 85.05 & 89.45 & 92.00 \\ Standard & 1.225 & 0.725 & 0.499 & 0.000 \\ Deviation & \multicolumn{4}{c}{} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Random Forest Classifier Summarized Results.  
The resulting tree (Figure 3) presents that Perceived Risk (X1) dictates the purchasing intention of safety tools among women. As the parent node, this will consider attitude (X0) as the child node with value less than or equal to 1.881 if parameters are satisfied and 0.123 otherwise. Following the tree diagram, satisfying the first child node will lead to X0, X1, and then subjective norm (X2). If the result is less than or equal to \(-0.909\), this will present very high purchasing intention of safety tools. If the conditions of the first child node are not satisfied, X1 and X2 will be considered with values less than or equal to \(-1.749\). This  
Figure 3: (**a**). Optimum Tree with Random Forest Classifier (True side). (**b**). Optimum Tree with Random Forest Classifier (False side).  
means that a positive purchasing intention will be evident if people have a heightened perception of risk and are influenced by the people around them.  
Satisfying the second child node, X0 (\(\leq\)0.123), X1 will be considered leading to high purchasing intentions. Otherwise, X2 and perceived behavioral control (X3) will be considered which will lead to very high purchasing intentions of safety tools. This result indicates that aside from a person's attitude, their own control would be positive if they perceive risk to be available. Moreover, if the second child node is not satisfied, X2 and X3 will be considered with high purchasing intentions. Otherwise, X1 and X0 will be considered leading to very high purchasing intentions.  
Overall, the latent variables such as attitude and perceived risk would promote a very high purchasing intention. On the other hand, subjective norm and perceived behavioral control will only lead to a high purchasing intention. Which means that the higher the risk, the higher other people will promote the purchasing of safety tools with positive response by the woman with their own behavioral control and positive attitude in purchasing.