## 3 Results and Discussion  
The properties of briquettes obtained from peanut shells and descriptive statistics about these properties are shown in Table 1. The dependent variable of deformation energy has a mean = 769.74 and std. dev. = 93.58. It is clear from Table 1 that the dependent variable is normally distributed. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Moisture** \\ **Content** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Density** \\ **(kg m\({}^{-3}\))** \\ \end{tabular} & \begin{tabular}{c} **Specific** \\ **Compression** \\ **Force** \\ **(N mm\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Shatter** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Compression** \\ **Resistance** \\ **(N)** \\ \end{tabular} & \begin{tabular}{c} **Tumbler** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Water** \\ **Resistance** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Compression** \\ **Stress** \\ **(N mm\({}^{-2}\))** \\ \end{tabular} &
\begin{tabular}{c} **Deformation** \\ **Energy** \\ **(\%)** \\ \end{tabular} \\ \hline count & 270 & 270 & 270 & 270 & 270 & 270 & 270 & 270 & 270 \\ \hline mean & 5.78 & 1125.08 & 74.42 & 94.08 & 3348.78 & 88.08 & 72.48 & 60.89 & 769.74 \\ \hline std & 1.03 & 152.79 & 16.06 & 3.87 & 722.60 & 3.91 & 7.95 & 13.14 & 93.58 \\ \hline min & 2.48 & 731.15 & 43.66 & 84.28 & 1964.66 & 77.98 & 51.58 & 35.72 & 526.32 \\ \hline max & 8.51 & 1493.01 & 111.50 & 99.99 & 5017.30 & 98.95 & 89.08 & 91.22 & 961.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptive statistics of briquettes. Figure 8: Illustration of the RF regression [68]. In this study, three different ensemble learning methods, Random Forest, Extreme Gradient Boosting, and CatBoost, were used. The performances of the models were interpreted based on the evaluation metrics obtained as a result of the established models. The training and testing results of the models are shown in Table 2. According to the model evaluation results, the training and testing phase values are close to each other. This indicates that there is no overfitting problem during the training phase and can also be considered an indication that the models can make consistent predictions. In the training dataset, the best values of MAE = 1.89, RMSE = 2.86, MAPE = 0.0024884, and R\({}^{2}\) = 0.9991 were obtained from the model established with the XGB method. According to the evaluation metrics, the success rankings in the training dataset are XGB, RF and CatBoost. In fact, it can be said that all models were very successful on the training dataset, and when each metric is evaluated on its own, it can be seen that the values obtained by the models are extremely close to each other. According to the test dataset results (see Table 2), the best R\({}^{2}\) = 0.9714 was obtained from the model established with the CatBoost method. Therefore, it can be said that the independent variables explain approximately 97.14% of the variance in the dependent variable. Since the R\({}^{2}\) value obtained is close to 1, the established model is successful. The R\({}^{2}\) values of the RF and XGB models are also high, at 0.9618 and 0.9585, respectively. The RMSE is a statistical metric used to calculate the error values of regression models. The RMSE values of the CatBoost, RF, and XGB models are 15.69, 18.14, and 18.91, respectively. While the best RMSE value was obtained from the model established with the CatBoost method, the results of the models established with the RF and XGB methods were close to each other. However, the model built with RF performed better than the model built with XGB, with a very small difference. The MAPE values obtained in all models were nearly 0.0146, but it can be said that the model established with CatBoost was more successful than the models established with other methods, albeit with a very small difference. According to the MAPE results, it can be said that the deformation energy of briquettes can be estimated with an error of 1.46%.