## 4 Conclusions  
In this paper, we have studied the performance of machine learning algorithms (RF, SVM, and GPR) in predicting the nuclear cross-section of \({}^{92}\text{Mo}(n,2N)\)\({}^{91}\text{Mo}\) nuclear reactions. We also studied the performance of machine learning algorithms using experimental and simulation input. We perform the feature selection step in determining the best number of predictors and a combination of predictors that give the highest correlation coefficient and lowest RMSE for our GPR, RF, and SVM machine learning models. We found that we do not need the whole experimental nuclear cross-section dataset to generate nuclear cross-section data that can fit ENDF/B-VIII.0 library data. It is generally known that training machine learning in the region outside of the explored range, either unknown data points or in between data points with high sparsity tend to fail to generalize, which indicates the dependency of the training on the regime studied. Thus, we introduce crude  
Figure 5: Comparisons between the EXP, EXP + CEP, and CEP using (**a**) GPR, (**b**) RF and (**c**) SVM algorithms. estimation property (CEP) dataset alongside experimental dataset (EXP) to increase the predictive abilities of the machine learning algorithms where it helps to reduce the sparsity of EXP dataset in between data points. Although not yet explored, it also has potential in extrapolating nuclear cross-section data by providing data points outside the explored range of incident energy in the EXP dataset. We found that EXP input works the best in training machine learning models to generate nuclear cross-section data despite being sparse and not covering the whole range of neutron incident energy (13 MeV-17 MeV). This is then followed by CEP and EXP + CEP input in terms of correlation coefficient and RMSE. The performance of GPR (RMSE = 0.33557) algorithms better follows by SVM (RMSE = 1.6296) and RF (RMSE = 7.4838) algorithms. We also find our machine performs better in generating the nuclear cross-section as compared to the simulation cross-section using TALYS 1.9 and EMPIRE 3.2 code. Conceptualization, M.A.B.H. and H.G.B. ; methodology, M.A.B.H. ; software, M.A.B.H. and Y.A.O. ; validation, M.A.B.H. and Y.A.O. ; formal analysis, M.A.B.H. and S.A.; investigation, M.A.B.H. and Y.A.O. ; resources, H.G.B. ; data curation, M.A.B.H. ; writing--M.A.B.H. and Y.A.O. ; writing--review and editing, H.G.B. and X.Y.C. ; visualization, M.A.B.H. and Y.A.O. ; supervision, H.G.B. and X.Y.C. ; project administration, H.G.B. and X.Y.C. ; funding acquisition, H.G.B. and X.Y.C. All authors have read and agreed to the published version of the manuscript. This research and the APC was funded by Yayasan Universiti Teknologi PETRONAS, grant number YUTP Cost Centre: 015 LC0-063. Not applicable. The authors express their appreciation to Yayasan Universiti Teknologi PETRONAS for the financial support for this study in the form of a research grant (YUTP Cost Centre: 015 LC0-063). The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **Input Data** & **RMSE** & \(\mathbf{R}^{2}\) \\ \hline \multirow{4}{*}{GPR} & [PERSON] et al. [17] & 18.996 & 0.98 \\ \cline{2-4}  & [PERSON] et al. [18] & 2.7569 & 0.99 \\ \cline{2-4}  & [PERSON] et al. [20] & 0.88192 & 0.99 \\ \hline \multirow{4}{*}{RF} & [PERSON] et al. [17] & 7.0876 & 0.99 \\ \cline{2-4}  & [PERSON] et al. [18] & 10.64 & 0.99 \\ \cline{2-4}  & [PERSON] et al. [20] & 6.5792 & 0.99 \\ \cline{2-4}  & [PERSON] et al. + [PERSON] et al. [18; 17] & 8.4342 & 0.99 \\ \cline{2-4}  & [PERSON] et al. + [PERSON] et al. [17; 20] & 7.1418 & 0.99 \\ \cline{2-4}  & [PERSON] et al. + [PERSON] et al. [18; 20] & 10.872 & 0.99 \\ \cline{2-4}  & [PERSON] et al. + [PERSON] et al. + [PERSON] et al. [17; 18; 20] & 10.598 & 0.99 \\ \hline \multirow{4}{*}{SVM} & [PERSON] et al. [17] & 38.494 & 0.92 \\ \cline{2-4}  & [PERSON] et al. [18] & 126.95 & 0.07 \\ \cline{1-1} \cline{2-4}  & [PERSON] et al. [20] & 1.1155 & 0.99 \\ \hline \hline \end{tabular}
\end{table}