## 3 Results and Discussions
This is due to the non-parametric nature of GPR algorithms, which are known to work well with a sparse dataset [21]. Hence, we found that the GPR is a much more versatile and robust machine learning algorithm as a prediction tool for our EXP, EXP + CEP, and CEP datasets as compared to the SVM and RF models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Input** & **Hyperparameters** \\ \hline \multirow{4}{*}{GPR} & \multirow{2}{*}{EXP + CEP} & Linear basis function, isotropic rational quadratic, 39.2269 kernal scale, \\  & & \(\sigma\) = 0.00010974 \\ \cline{2-3}  & & Radial basis function, non-isotropic matern 3/2, 114.1658 kernal scale, \\  & & \(\sigma\) = 1246.4488 \\ \cline{2-3}  & & CEP & Radial basis function, non-isotropic matern 3/2, 249.3891 kernal scale, \(\sigma\) = 3.477 \\ \hline \multirow{4}{*}{RF} & EXP & Bagged tree, 3 leaves, 10 learners and 2 predictors \\ \cline{2-3}  & EXP + CEP & Bagged tree, 2 leaves, 172 learners and 4 predictors \\ \cline{2-3}  & CEP & Bagged tree, 1 leaf, 13 learners and 4 predictors \\ \hline \multirow{4}{*}{SVM} & EXP & Quadratic kernel funcion, 992.923 box constraints, \(\varepsilon\) = 1.1308 \\ \cline{2-3}  & EXP + CEP & Linear kernel function, 0.01304 box constraints, \(\varepsilon\) = 1.5412 \\ \cline{2-3}  & CEP & Cubic kernel function, 150.2836 box constraints, \(\varepsilon\) = 5.6412 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameters optimization using Bayesian Optimization. Figure 2: Graph of prediction **vs.** measured nuclear cross-section of \({}^{92}\text{Mo}(n,2N)\)\({}^{91}\text{Mo}\) for GPR machine learning algorithm. The black line is the ENDF/B-VIII.0 library data while the red dot is the predictions done by the GPR algorithms with **(a)** EXP, **(b)** CEP and **(c)** EXP + CEP as input. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **Input** & \(R^{2}\) & **RMSE (mB)** \\ \hline \multirow{3}{*}{GPR} & EXP & 1 & 0.33557 \\ \cline{2-3}  & EXP + CEP & 1 & 7.4093 \\ \cline{2-3}  & CEP & 1 & 0.86292 \\ \hline \multirow{3}{*}{RF} & EXP & 1 & 7.4838 \\ \cline{2-3}  & EXP + CEP & 1 & 7.7885 \\ \cline{2-3}  & CEP & 1 & 7.6552 \\ \hline \multirow{3}{*}{SVM} & EXP & 1 & 1.6296 \\ \cline{2-3}  & EXP + CEP & 1 & 5.9236 \\ \cline{2-3}  & CEP & 1 & 4.3059 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The statistical analysis of the performance of GPR, RF, and SVM algorithms using EXP, EXP + CEP and CEP input. The machine learning model with \(R^{2}\) closed to 1 and lowest RMSE is highlighted in yellow. Figure 3: Graph of prediction vs. measured nuclear cross-section of \({}^{92}\)Mo\((n,2N)\)\({}^{91}\)Mo for RF machine learning algorithm. The black line is the ENDF/B-VIII.0 library data while the red dot is the predictions done by the RF algorithms with (**a**) EXP, (**b**) CEP and (**c**) EXP + CEP as input. In Figure 5, we plotted the predicted cross-section of GPR algorithms using EXP, EXP+CEP, and CEP inputs together with the experimental nuclear cross-section, simulated cross-section and ENDF/B-VIII.0 library data. From Figure 5, we can observe that the prediction done by the GPR algorithms fit better with ENDF/B-VIII.0 library data compared to the experimental and simulation nuclear cross-section (TALYS 1.9 and EMPIRE 3.2). Similar observation goes to the generated cross-section using RF and SVM algorithms for all inputs. This indicates a potential use of training machine learning models using simulated input instead of the experimental dataset for nuclear reactions that are hard to be experimented with or unavailable in the EXFOR database. This is useful for both interpolation and extrapolation of nuclear cross-section; however, there are limitations of our work. We have not yet tested machine learning on the region beyond what is provided by EXFOR. Error bar of the experimental cross-section from EXFOR is also not included here; however, it is possible to simulate the confidence band using machine learning algorithms.