## 4 Results
### Detailed Results  
In addition to presenting a performance for grouped data, an in-depth comparison between the models' predictions and experimental values for the elastic modulus separated for each type of fiber reinforcement is shown in Figures 9 and 10 and Table 6. The plots depicted in the figures show the predicted values on the vertical and the experimental values on the horizontal axis. The table displays several statistics, including the Pearson correlation coefficient (\(r\)) [73], the coefficient of determination (\(R^{2}\)) [74], and the result of a Student's \(t\)-test [75] with the null hypothesis stating that there is no significant difference between the predicted and experimental values (with 0.05 significance level). The metrics and the adjustments between predicted and experimental values for the carbon fiber (CRTP) highlight the poor performance of the linear models (RDG and LAS) and the Bayesian model. For all of them, the r was inferior to 0.40, the (\(R^{2}\)) lower than 0.20, and the null hypothesis was rejected in the Student's \(t\)-test. The results for SVR and DTR highlight their average performance, while the KNN, RFR, and CAT stand out with acceptable correlation metrics. However, since the null hypothesis was rejected in the Student's \(t\)-test for KNN, only the tree-based models can be considered sufficiently robust for performing the task. The same pattern is observed for the fiberglass, where RFR and CAT stood out from the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target** & **Model** & **Prediction Time (ms)** & **Model Size (kB)** \\ \hline E & BAY & 0.2464 \(\pm\) 0.0655 & 1.8690 \(\pm\) 0.0000 \\ E & RDG & 0.2571 \(\pm\) 0.1237 & 1.0540 \(\pm\) 0.0000 \\ E & LAS & 0.3108 \(\pm\) 0.2160 & 1.1542 \(\pm\) 0.0004 \\ E & DTR & 0.2475 \(\pm\) 0.0485 & 2.6079 \(\pm\) 0.0808 \\ E & SVR & 0.3114 \(\pm\) 0.0791 & 9.8657 \(\pm\) 0.3070 \\ E & RFR & 2.5034 \(\pm\) 0.8491 & 323.2075 \(\pm\) 107.4422 \\ E & KNN & 13.2900 \(\pm\) 0.3737 & 12.1362 \(\pm\) 0.0033 \\ E & CAT & 1.0333 \(\pm\) 0.3281 & 213.9868 \(\pm\) 114.2556 \\ \(\sigma\) & BAY & 0.2542 \(\pm\) 0.0797 & 1.8690 \(\pm\) 0.0000 \\ \(\sigma\) & RDG & 0.2395 \(\pm\) 0.0714 & 1.0540 \(\pm\) 0.0000 \\ \(\sigma\) & LAS & 0.2494 \(\pm\) 0.0535 & 1.1542 \(\pm\) 0.0004 \\ \(\sigma\) & DTR & 0.2458 \(\pm\) 0.0525 & 2.5439 \(\pm\) 0.1393 \\ \(\sigma\) & SVR & 0.3184 \(\pm\) 0.0753 & 10.5268 \(\pm\) 0.0322 \\ \(\sigma\) & KNN & 13.3308 \(\pm\) 0.3256 & 12.1356 \(\pm\) 0.0024 \\ \(\sigma\) & CAT & 0.9430 \(\pm\) 0.2397 & 198.7430 \(\pm\) 102.1022 \\ \(\sigma\) & RFR & 2.8011 \(\pm\) 0.9439 & 349.6818 \(\pm\) 88.7123 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Computational performances of the models when predicting E and \(\sigma\). other models. On the other hand, for the kevlar fiber only the KNN passed the Student's \(t\)-test and achieved good correlation metrics. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Fiber Type** & **Metric** & **SVR** & **DTR** & **RFR** & **CAT** & **KNN** & **RDG** & **LAS** & **BAY** \\ \hline  & \(r\) & 0.5835 & 0.6059 & 0.8062 & 0.8075 & 0.7972 & 0.3667 & 0.3473 & 0.3585 \\ CRTP & \(R^{2}\) & 0.3404 & 0.3672 & 0.6500 & 0.6521 & 0.6355 & 0.1367 & 0.1206 & 0.1285 \\  & Student’s \(t\)-test & - & - & Ok & Ok & - & - & - & - \\ \hline  & \(r\) & \(-\)0.0075 & 0.0218 & 0.1100 & 0.1091 & \(-\)0.0849 & 0.0250 & \(-\)0.019 & \(-\)0.0370 \\ FGRTP & \(R^{2}\) & 0.001 & 0.3672 & 0.6500 & 0.6521 & 0.6355 & 0.1367 & 0.1206 & 0.1285 \\  & Student’s \(t\)-test & - & - & Ok & Ok & - & - & - & - \\ \hline  & \(r\) & 0.5240 & 0.4342 & 0.5789 & 0.6695 & 0.7870 & 0.2731 & 0.2546 & 0.2715 \\ KrRTP & \(R^{2}\) & 0.2746 & 0.1885 & 0.3351 & 0.4482 & 0.6719 & 0.0746 & 0.0648 & 0.0737 \\  & Student’s \(t\)-test & - & - & - & Ok & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Detailed results for the prediction of \(E\). Figure 9: Comparison for predicted vs. experimental Elastic modulus, GPa, part 1.