## 3 Results and Discussions  
Three algorithms are applied in this study which are the random forest (RF), Gaussian process regression (GPR), and support vector machine (SVM). Random forest is a supervised learning algorithm that utilizes the recursive partitioning of regression trees. For non-linear regression problems, the ensemble method is used to grow trees, where successive trees are independent of previous trees. Instead, each tree is independently determined using a bootstrap sample of datasets. Gaussian process regression is a type of supervised learning algorithm that is based on Bayesian non-linear regression. The GPR algorithm is non-parametric, where probability distributions are surmised over all possible values of x. Theoretically, the data points can be represented as multivariate Gaussian distribution form where GPR is performed to constrain possible form of covariance function and covariance matrix. Support vector machine was first formulated as supervised learning that utilizes hyperplanes for the separation of classes. Essentially, SVM transforms a non-linear regression into linear regression by mapping input space to new feature space by using kernel functions. After classifying data points into clusters using hyperplanes, the algorithms draw a line or hyperplanes that minimize the error or loss function. The performance for each algorithm is compared by using the root mean square error (RMSE) as a quality criterion. The relationship of RMSE evaluated for each algorithm with different combination of experimental data and computational data used as input is reported. However, the experimental dataset provided by EXFOR for \({}^{92}\)Mo(n,2n)\({}^{91}\)Mo nuclear reaction mostly consists of single data points, which are shown in Figure 1. This leads \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Variable Name** & **No of Data Points** & **Variable Type** \\ \hline \(\chi_{1}\) & Incident Energy (MeV) & 41 & Input \\ \hline \(\chi_{2}\) & EMPIRE 3.2 output (mB) [3] & 41 & Input \\ \hline \(\chi_{3}\) & TALYS 1.9 output (mB) [3, 19] & 82 & Input \\ \hline \(\chi_{4}\) & [PERSON] et al. [17] & 7 & Input \\ \hline \(\chi_{5}\) & [PERSON] et al. [18] & 11 & Input \\ \hline \(\chi_{6}\) & [PERSON] et al. [20] & 21 & Input \\ \hline \(Y_{1}\) & ENDF/B-VIII.0 library (mB) [16] & 41 & Output \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Variables used as the input and output for prediction of nuclear cross-section data of \({}^{92}\)Mo(\(n,2N\)) \({}^{91}\)Mo. The experimental dataset (EXP) includes experimental data from [PERSON] et al. [17], [PERSON] et al. [18] and [PERSON] et al. [20] while the crude estimation property (CEP) includes EMPIRE 3.2 and TALYS 1.9 data from [PERSON] and [PERSON]\({}^{3}\) and TALYS1.9 data from [PERSON] et al. [19]. Figure 1: Graph of nuclear cross-section data of \({}^{92}\)Mo(\(n,2N\)) \({}^{91}\)Mo nuclear reaction from various literature used in this work [3, 17, 18, 19]. The neutron incident energy, \(E_{n}\) is between 13 MeV to 17 MeV. to a very sparse dataset, especially after the removal of the incomplete data points when being fed into machine learning algorithms. Thus, we find it is important to selectively choose the dataset used as the input. The first step is to remove the experimental nuclear cross-section input data sets that have only single data points. Here, we are left with an experimental nuclear cross-section dataset by [PERSON] et al. (1976), [PERSON] et al. (1969) and [PERSON] et al. (1972). To obtain our results, the feature selection is done by Recursive Feature Elimination (RFE) where RMSE and correlation coefficient served as an estimator to determine the best dataset and number of the dataset used as a predictor. The same step is repeated for the CEP-only dataset and a combination of experimental nuclear cross-section and CEP. The results of the feature selection are tabulated in Tables 1-3.