## 3 Results and Discussion  
The objective of this section is to summarise the results obtained for the prediction of E\({}_{\text{u},50}\) using the SVM, ANN, RF and MR algorithms with MR serving as the baseline for comparison in this work. These algorithms are analysed using criteria such as accuracy--metrics, computational efficiency--time and interpretability--sensitivity analysis. This provided a sufficient understanding of the applicability of these models in predicting E\({}_{\text{u},50}\) of fibre-reinforced soil-cement mixtures. Table 2 lists the hyperparameters and computational times for the ML algorithms. This shows that MR and SVM have rapid computational times, averaging of 0.32 s and 0.06 s, respectively, for the five runs. ANN and RF took about 2 s on average for the five runs. This indicates that using the ANN and RF algorithms to learn the problem takes time relative to SVM and MR.  
As mentioned earlier, metrics are used to analyse the accuracy of the algorithms. Table 3 demonstrate that the ANN and RF algorithms have an especially good determination coefficient, based on R\({}^{2}\) close to one. ANN has the lowest mean absolute error (MAE) among all four trained ML algorithms. This is followed by SVM, then RF and finally MR. The result is further explained by the REC curve in Figure 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Hyperparameter** & **Time (s)** \\ \hline \multicolumn{3}{c}{**Young Modulus (E\({}_{\text{u},50}\))**} & **Young Modulus (E\({}_{\text{u},50}\))** \\ \hline ANN & H = 3 \(\pm\) 1 & 2.01 \(\pm\) 0.045 \\ \hline SVM & \(\gamma\) = 0.16 \(\pm\) 0.01 & **0.06 \(\pm\) 0.02** \\ \hline RF & Mtry = 5 \(\pm\) 1 & 2.31 \(\pm\) 0.19 \\ \hline MR & - & **0.32 \(\pm\) 0.03** \\ \hline NB.: Best Value in Bold. & & \\ \end{tabular}
\end{table} 
Table 2: Hyperparameters and computational time for ML model. The REC is used in this study to determine the regression error for the ML algorithms. The best REC curve shows an accuracy of one on the \(y\)-axis, which is achieved for the lowest possible absolute deviation on the \(x\)-axis. Figure 3 compares the REC curves from the ML algorithms used in this study, confirming the better response of SVM and the lower response of MR. Thus, for an absolute deviation of 200 kPa in the elastic modulus, SVM shows a better response than the other algorithms. RF and MLP (ANN) show comparable results for this absolute deviation. Figure 4 shows the correlation between the experimental values of E\({}_{\text{u},50}\) and the predicted results for the four ML algorithms. From its analysis, although some dispersions can be observed, the overall performance is very promising as shown by the calculated metric, namely by the R\({}^{2}\) values close to 1, particularly according to the ANN model (see Table 3). The ANN and RF models showed the best correlation based on the determination coefficient, as indicated in Table 3 above. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \multicolumn{3}{c}{**Young Modulus (E\({}_{\text{u},50}\))**} \\ \hline  & **MAE** & **RMSE** & **R\({}^{2}\)** \\ \hline ANN & **103.52** & **207.66** & **0.95** \\ SVM & 106.14 & 230.45 & 0.88 \\ RF & 108.77 & 242.67 & 0.93 \\ MR & 139.21 & 261.85 & 0.81 \\ \hline \hline \end{tabular}  
* NB.: Best Value in Bold.  
\end{table} 
Table 3: Metrics for ML algorithms. Figure 3: REC curves comparing all four ML models for Young’s Modulus prediction. As already mentioned, interpretability is one of the most important aspects when explaining an algorithm. For this reason, a sensitivity analysis was conducted for all ML algorithms. Relative importance was generated to identify the relevant variables (inputs) that contribute to the predictions of \(\text{E}_{\text{u}.50}\) for the four ML algorithms, as shown in Figure 5. Figure 5 helps to understand what was learned by the algorithm and compare it to empirical knowledge.