## 4 Results and Discussion
### ML Classification
#### 4.3.3 Feature Importance  
Indicators of the importance of independent features in ML prediction can also give an insight into the data used in the modelling as well as allow for an improvement of the efficacies of the models adopted. The relative significance and usefulness of the input variables used in the predictions of target features are depicted in Figure 15 for both the regression and multiclass classification ML predictions. The effect of each individual independent variables is herein assessed using the best prediction models. In terms of ML modelling and prediction of the intrinsic compressibility index of soils, it is clearly observed from Figure 15a that the feature of importance in this regard is the ratio of voids at 100 kPa of applied pressure. This is hardly surprising given the direct bearing of this measure quantity on C*c as previously demonstrated in the void index relationship. Among the other variables of indirect importance, the liquid limit including its corresponding void ratio prove to be the next most useful features in the forecast of C*c. It is very imperative to bear in mind that several strong linear relationships exist between the Arterberg limits and C*c as suggested by previously mentioned studies. On the other hand, when used for the first time in the prediction of C*c, the specific gravity or the texture element seems to bear the least influence at least within the remit of this research.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**BDT**} & \multicolumn{2}{c}{**RDF**} & \multicolumn{2}{c}{**LR**} & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**VE**} & \multicolumn{2}{c}{**SE**} \\ \cline{2-13}  & **10 CV** & **5 CV** & **10 CV** & **5 CV** & **10 CV** & **5 CV** & **10 CV** & **5 CV** & **10 CV** & **5 CV** & **10 CV** & **5 CV** \\ \hline Accuracy & 0.898 & 0.907 & 0.898 & 0.866 & 0.747 & 0.731 & 0.721 & 0.690 & **0.941** & **0.958** & **0.933** & **0.924** \\ Precision & 0.869 & 0.805 & 0.836 & 0.772 & 0.531 & 0.523 & 0.523 & 0.455 & **0.859** & **0.865** & **0.856** & **0.787** \\ Recall & 0.878 & 0.836 & 0.842 & 0.715 & 0.634 & 0.570 & 0.585 & 0.520 & **0.861** & **0.856** & **0.856** & **0.758** \\ \hline \hline \end{tabular}
\end{table}
Table 9: **Sensitivity** metrics for traditional ML algorithms and meta-ensembles with dataset training and validation done under k-fold CV method.  
Figure 15: _Cont._In terms of multiclass classification and when the average of the absolute class weights of the variables are considered, Figure 15b indicates that the Atterberg limits of the soils are the most significant factors in the determination of soil classes with the plasticity index wielding or having the highest numerical importance. This is a confirmation of the established research and theory that uses mostly the soil plasticity index for the direct and indirect determination of soil classes especially if the classification is carried out based on a standard provided by the unified soil classification system (USCS). By virtue of individual class vectors, the Atterberg limits features of the soils do generally seem to carry the most influence on the multiclass prediction of the soil categories as observed in Figure 15c. It is also interesting to note the individual importance of the ratio of voids at PI on the prediction of the relatively least plastic soils when compared to the actual Atterberg limits (both PI and LL). Again, as could be observed, the soil textural characteristics does not seem to be a lot useful in the prediction of the soil classes not to mention the derived features C*c.