## 3 Results and Discussion
When we evaluate the models according to the MAE metric, it can be said that although the values are extremely close to each other, the model established with the RF method was more successful than other models with a small difference. For the testing phase, the model established with the CatBoost method was more successful in all metrics except the MAE metric. As mentioned before, the models obtained similar values in the MAE metric. As a result, it can be said that the most successful results in the testing partition were obtained from models established with the CatBoost method. Figure 9 shows the line plots of the models. Line plots created with training data are in the left column, and line graphs created with test data are in the right column. In the graphs, the \(x\)-axis in the graphs shows the observations, and the \(y\)-axis shows the deformation energy. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Evaluation Criteria**} \\ \cline{2-9}  & \multicolumn{4}{c}{**Train**} & \multicolumn{4}{c}{**Test**} \\ \cline{2-9}  & **MAE** & **RMSE** & **R\({}^{2}\)** & **MAPE** & **MAE** & **RMSE** & **R\({}^{2}\)** & **MAPE** \\ \hline CatBoost & 6.10 & 8.25 & 0.9921 & 0.0080654 & 10.81 & 15.69 & 0.9714 & 0.0145815 \\ Extreme Gradient Boosting & 1.89 & 2.86 & 0.9991 & 0.0024884 & 10.72 & 18.91 & 0.9585 & 0.0145850 \\ Random Forest & 4.16 & 7.14 & 0.9936 & 0.0056641 & 10.63 & 18.14 & 0.9596 & 0.0146270 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ensemble learning evaluation results. While red lines show actual values, dashed blue lines show predicted values. The lines nearly overlap in all three plots in the left column. Accordingly, the models built were quite successful on the training dataset. These results also support the results of the evaluation metrics of the training dataset in Table 2. The lines nearly overlap in all three plots in the right column. Accordingly, the models established were also quite successful on the test dataset. These results also support the results of the evaluation metrics of the test dataset in Table 2.  
Figure 9: Line plots of modeling results. No studies have been found that predict biriquette deformation energy using machine learning methods. In the field literature, [PERSON] et al. [2] estimated the compressive resistance of briquettes via ensemble learning methods. The best R\({}^{2}\), MAPE, MAE, and RMSE values were obtained from the model established with the Extra Trees method; these values were 0.76, 0.0799, 284.01, and 368.1, respectively. [PERSON] et al. [52] estimated the deformation energy of cherry tomatoes using machine learning methods. The best results were obtained from models established with the Artificial Neural Networks method (R\({}^{2}\) = 0.97, MAE = 0.051, MSE = 0.005). In this study, the deformation energies of briquettes obtained from peanut shells were estimated via ensemble learning methods. The models built according to the results of both the training and test datasets were extremely successful, and accordingly, it can be said that machine learning methods can be used to estimate the briquette deformation energy.