## 3 Results and Discussions
By using RFE, we can decrease the number of features used while increasing the performance of our machine learning model which can be seen in Tables 1 and 2. For the RFE done on the combination of CEP and EXP in Table 3, we will only consider the combination of CEP with a single experimental dataset since the combination of two and three EXP dataset does not give better performance in terms of RMSE and correlation coefficient as shown in Tables 1 and 2. From Tables 1-3, the best selection of features is highlighted in yellow. After performing the RFE step, Bayesian optimization of the hyperparameters is performed, with up to 100 iterations of the hyperparameters, choosing the ones that minimize CV error. For RF, the methods in which trees are constructed using bagged tree where multiple trees are constructed by randomly training independent variable composed of the same size. The range of search for the number of leaves, number of learners, number of a predictor to sample for RF are set in between 1-34, 10-500, 1-3 and 0.001-1. For GPR, the Bayesian optimization is performed with a search range of three different basis functions which are constant, radial, and linear, four different kernel functions which are rational quadratic, exponential, squared exponential, and the Matern kernel function with parameters of either \(3/2\) or \(5/2\). Kernel scale is set in between 0.4166 to 416.6 and noise standard deviation; \(\sigma\) is set in between 0.0001 to 1248.651. For SVM, the Bayesian optimization is performed with a search range for box constraint and kernel scale in between 0.001 to 1000, \(\varepsilon\) in between 0.164 to 16,400.0208. Kernel function considered is Gaussian, linear, quadratic, and cubic. From the Bayesian optimization performed, we determined the optimized hyperparameters of each machine learning algorithm with different input in Table 2. The performance of three proposed machine learning algorithms has been investigated using the correlation coefficient and RMSE as reported in Table 3. We can observe from Table 3, for EXP, EXP + CEP and CEP input, the GPR algorithms have the highest correlation coefficient with \(R^{2}=1\) and the lowest RMSE compared to the RF and SVM algorithm. Among those three inputs, the EXP input displayed better prediction capabilities with \(\text{RMSE}=0.33557\) mb followed by CEP input with \(\text{RMSE}=0.86292\) mb and EXP + CEP input with \(\text{RMSE}=7.4093\) mb. RF and SVM algorithms also exhibit the same trends where the EXP input performs better in terms of RMSE followed by CEP and EXP + CEP. For RF, the differences of RMSE for all three inputs are small with RMSE ranging from 7.4838 to 7.885. This indicates little change in terms of performance regardless of the type of inputs being fed into the model. Figures 2-4 depict the predicted vs. actual plot where we can observe the accuracy of the prediction made by GPR, RF and SVM algorithms, respectively. From Figures 2-4, we observed the comparisons of the performances of our machine learning models for the three different types of input (EXP, CEP and EXP + CEP), respectively. For GPR, the predicted nuclear cross-section shows a good fit with ENDF/B-VIII.0 library data points when being fed input sets, with \(R^{2}=1\). The performance of the RF model is similar for all three different inputs, where it shows a good fit with ENDF/B-VIII.0 library data points (\(R^{2}=1\)) as it can be seen in Figure 3a-c. This might be attributed to the property of RF algorithms that build multiple decision trees (bagging) and averaged the RMSE of all of the individual decisions tree. SVM algorithms also show good fits using all three inputs with EXP are the best input, followed by CEP and EXP + CEP. The GPR gives generally a much better prediction followed by SVM and RF with EXP, EXP + CEP and CEP inputs. GPR models can give a good fit even with the unavailability of experimental data of EXP input over a wide range of \(E_{n}\) as shown in Figure 2a.