## 3 Results and Discussion  
The determination of briquette quality and monitoring them throughout the whole supply chain is a highly difficult, expensive, and crucial issue. Briquettes have become more and more popular among other biofuels in recent years due to their low storage costs and excellent combustion efficiency. In this study, machine learning techniques were used to quickly and inexpensively assess compression resistance, which is one of the key briquette quality factors, as opposed to expensive and time-consuming laboratory research. The model evaluation used to verify the data obtained by Extra Trees, Random Forest and  
Figure 6: K-Fold cross-validation diagram [42].  
Light Gradient Boosting methods with experimental data is given in Table 2. When the MAPE and R2 values are examined, the best result among the three models is obtained from the Extra Trees model. This model achieved the highest R2 value of 0.7595. It is expected that this value will be close to 1.  
In the validation of the model, the 10-fold cross-validation method was used, and the average value of the results obtained was considered. The K-Fold validation results for all three models are given in Table 3. When the table is examined, it is seen that the standard deviation (std) values of R2 and MAPE are low. This is true for all three models.  
The graph of the measured compressive resistance values of the biquettes produced from peanut shells and the values predicted by the Extra Trees model, which gives the best results, are given in Figure 7. As a result of the estimation made with the test data, the best result was obtained with the value of R2 = 0.754.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Regression Model** & **R2** & **MAPE** & **MAE** & **RMSE** \\ \hline Extra Trees & 0.7595 & 0.0799 & 284.0107 & 368.1077 \\ Random Forest & 0.7287 & 0.0845 & 300.6592 & 390.9278 \\ Light Gradient Boosting & 0.6838 & 0.0922 & 328.6430 & 422.0057 \\ Regression Model & R2 & MAPE & MAE & RMSE \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model evaluation parameters of machine learning models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Extra Trees** & \multicolumn{2}{c}{**Random Forest**} & \multicolumn{2}{c}{**Light Gradient Boosting**} \\ \hline
**Fold** & **R2** & **MAPE** & **R2** & **MAPE** & **R2** & **MAPE** \\ \hline
0 & 0.7610 & 0.0843 & 0.7346 & 0.0873 & 0.6932 & 0.0953 \\
1 & 0.7315 & 0.080  
In Figure 8, the estimation errors are given for the Random Forest model, and the R\({}^{2}\) value calculated on the test data for this model, which is the second-best model for the estimation of compressive resistance, was 0.712. I has been stated in the literature that the Random Forest model gives good results [39; 44; 45].  
In this study, it is the Light Gradient Boosting model with an R\({}^{2}\) = 0.670 value that gives the third best result and is acceptable for strength estimation. The prediction error values of this model are given in Figure 9. As a result, it can be said that all models demonstrated significant non-linear mapping generalization abilities and are useful for predicting compressive resistance. The optimization of hyperparameters or experimentation with alternative models may be attempted to improve the machine learning model. The variation in briquette parameters is influenced by both the experimental conditions and the choice of raw material.