## 3 Result
### Comparison of Predictive Model Performance  
This study compares the direct regression performance of fundamental machine learning models, including Random Forest (RF), Support Vector Machine (SVM), XGBoost (XGB), and Light Gradient Boosting Machine (LGBM). Additionally, it contrasts the efficacy of the proposed classification-regression stacking approach with direct regression models. The comparative results of model performance, along with the best outcomes achieved by various models, are depicted in Figure 12, while the top-performing metrics of different models are summarized in Table 4. The box plot in Figure 12 visually represents the distribution of model performance indicators across various models. The Bland-Altman plot of predicted results from categorical regression stacking of models is shown in Figure 13.  
Figure 11: Figure (**A**) shows the accuracy of the proposed classification model, which worked as NVG-cluster, NVG, HVG-cluster, and HVG, four different feature inputs under different feature numbers. Figure (**B**) shows the confusion matrix for classification models using NVG and clustered features.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature Input** & **Feature Selection Method** & **Classifier** & **Number of Features** & **Accuracy** & **Precision** \\ \hline \multirow{2}{*}{NVG—Clustering Features} & & & 10 & 90.71\% & 75.00\% \\  & reliefF & SVM & 15 & 93.35\% & 88.28\% \\  & & & 20 & 91.00\% & 76.08\% \\ \hline \multirow{2}{*}{NVG} & & & 10 & 87.98\% & 75.66\% \\  & reliefF & SVM & 15 & 87.62\% & 73.34\% \\  & & & 20 & 85.97\% & 74.64\% \\ \hline \multirow{2}{*}{HVG—Clustering Features} & & & 10 & 68.00\% & 65.37\% \\  & & & 15 & 75.29\% & 68.26\% \\  & & & 20 & 63.14\% & 62.7\% \\ \hline \multirow{2}{*}{HVG} & & & 10 & 53.13\% & 58.12\% \\  & & & 15 & 62.76\% & 60.15\% \\  & & & 20 & 52.27\% & 51.70\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of Classification Model Performance.  
Figure 12: These figures are individual model indicator box plots. Figure (**A**) shows a comparison for MAE, Figure (**B**) shows a comparison for R2, and Figure (**C**) shows a comparison for RMSE.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Type** & **Feature Selection Method** & **Number of Features** & **MAE (g/L)** & **RMSE (g/L)** & **R2 (0–1)** \\ \hline \multirow{3}{*}{RF} & & 10 & 8.44 & 12.04 & 0.22 \\  & mRMR & 20 & 8.60 & 11.57 & 0.28 \\  & & 30 & 8.18 & 11.06 & 0.37 \\ \hline \multirow{3}{*}{SVM} & & 10 & 8.92 & 11.87 & 0.24 \\  & mRMR & 20 & 10.06 & 13.41 & 0.11 \\  & & 30 & 11.72 & 15.32 & 0.05 \\ \hline \multirow{3}{*}{XGB} & & 10 & 9.51 & 13.02 & 0.14 \\  & mRMR & 20 & 10.72 & 13.32 & 0.11 \\  & & 30 & 11.01 & 13.85 & 0.06 \\ \hline \multirow{3}{*}{SVM-XGB} & & 10 & 7.06 & 9.28 & 0.63 \\  & mRMR & 20 & 7.94 & 10.15 & 0.54 \\  & & 30 & 8.25 & 10.92 & 0.49 \\ \hline \multirow{3}{*}{SVM-LGBM} & & 10 & 6.70 & 8.21 & 0.64 \\  & mRMR & 20 & 7.00 & 8.66 & 0.58 \\  & & 30 & 7.30 & 8.83 & 0.57 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative Results of Performance of Different Detection Models.  
Figure 13: The scatterplot of the results of the categorical regression confounding model using the new features as well as the Altman plot are presented in this figure.