## 4 Results and Discussion
Measures and predictions obtained after the incorporation of the PCA technique, which reduces the initial eight parameter space to six, are shown in Table 7 and Figure 6. If examined individually, Table 7 follows the same trend as the values in Table 6. The tree-based algorithms have achieved maximum measures of accuracy compared to linear- and kernel-based ones. If these values are compared to the eight-input case in Table 6, it is observed that \(R^{2}\) scores are slightly lower for all cases. From a general point of view, PCA has trivial impact on linear- and kernel-based algorithms, e.g., the algorithms that have shown poor performance during the eight-input data flow (see Table 6 and Figure 5). Figure 5: Experimental versus predicted values for \(f_{cc}\) (both training and test data) in identity plots being the output of 12 different algorithms, (**a**) MLR, (**b**) LASSO, (**c**) Ridge, (**d**) SVR-linear kernel, (**e**) SVR-RBF, (**f**) SVR-polynomial, (**g**) GP, (**h**) k-NN, (**i**) DT, (**j**) RF, (**k**) GBR, and (**l**) MMLP. The 45\({}^{\circ}\) line shows the perfect match. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\mathbf{R^{2}}\) & **MAE** & **MSE** & **AAD** & **AIC** \\ \hline MLR & 0.749 & 19.01 & 26.39 & 23.38 & 2769.66 \\ Lasso & 0.748 & 18.92 & 26.32 & 23.57 & 2768.43 \\ Ridge & 0.749 & 19.01 & 26.39 & 23.38 & 2769.62 \\ SVR-lin & 0.749 & 17.31 & 26.56 & 20.19 & 2774.11 \\ SVR-rbf & 0.790 & 16.63 & 26.31 & 21.43 & 2753.01 \\ SVR-poly & 0.883 & 12.82 & 18.66 & 15.17 & 2565.71 \\ GP & 0.748 & 20.02 & 27.59 & 26.69 & 2795.89 \\ k-NN & 0.883 & 10.42 & 18.69 & 12.19 & 2564.77 \\ DT & 0.880 & 10.38 & 19.12 & 12.18 & 2579.17 \\ RF & 0.903 & 9.72 & 17.13 & 11.91 & 2514.28 \\ GBR & 0.883 & 9.97 & 18.67 & 12.07 & 2566.18 \\ MLP & 0.885 & 12.57 & 18.60 & 15.18 & 2559.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Calculated metrics for all ML algorithms applied to the dataset for the reduced case with six PCA inputs. Figure 6: Experimental versus predicted values for \(f_{cc}\) (both training and test data, after the incorporation of six PCA inputs instead of the original eight) in identity plots being the output of 12 different algorithms, (**a**) MLR, (**b**) LASSO, (**c**) Ridge, (**d**) SVR-linear kernel, (**e**) SVR-RBF, (**f**) SVR-polynomial, (**g**) GP, (**h**) k-NN, (**i**) DT, (**j**) RF, (**k**) GBR, and (**l**) MMLP. The 45\({}^{\circ}\) line shows the perfect match. Nevertheless, there is significant loss of accuracy in terms of RMSE, MAE, and AAD and for RF, DT, GBR, MLP, and SVR-poly (see Table 8). The k-NN has also shown significant RMSE increase. It has to be noted that the application of the PCA technique here is given for comparison reasons. The number of available experimental data and the eight input features do not primarily demand increased computational time to run on modern hardware sources, and all inputs can be processed effectively. However, it is clearly depicted that during a computational process of predicting material properties, when and if needed, PCA can be successfully introduced. The experimental research efforts from our team continue and future work is going to enrich the dataset with more input and output properties, and this is certainly a field of application. As one of the primary goals of materials science and engineering research is to develop novel materials that perform optimally under specific conditions, it's important to understand how and which features affect its behavior. Results can be found in sensitivity analysis to identify the importance of each input variable in the prediction process. Taking in mind all (eight) model input parameters, characteristic feature importance plots to argue on each input significance on the final prediction are presented next. This investigation is made on an algorithmic basis. Figure 7 presents the most important input features for four of the algorithms investigated here by incorporating the _FeatureImportances()_ function from the YellowBrick python package [55].