## 5 Results and Discussion
### Support Vector Machine Model Results  
The results of the ML model implemented utilizing the SVM to classify different soil types are presented here. The confusion matrix computed with the SVM model to evaluate its effectiveness is presented in Table 9. The confusion matrix shows that the model predicted almost all instances correctly, with a few misclassifications in each soil type. The overall accuracy of the model is very high (almost 100%), indicating that it is a high-performing model.  
Table 10 shows the class distribution summary for the SVM model. The model's sensitivity is high for each soil type, indicating that the model is good at correctly identifying the positive cases for each soil type. The model's specificity is also high for all soil types, indicating that the model is good at correctly identifying the negative cases for all soil types. Moreover, the model's balanced accuracy (the average of sensitivity and specificity) is remarkably high (almost 1) for all soil types. This shows that the model can accurately identify both positive and negative cases, making it a reliable classifier for the soil classification task.  
The model exhibits high PPVs for all soil types, indicating its strong ability to predict the samples of specific soil types accurately. Similarly, the model shows high NPVs for  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Performance Metrics** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline Sensitivity & 1 & 1 & 1 & 1 & 1 & 0.97 \\ Specificity & 1 & 1 & 1 & 1 & 1 & 1 \\ PPV & 1 & 1 & 1 & 1 & 1 & 0.93 \\ NPV & 1 & 1 & 1 & 1 & 1 & 1 \\ Prevalence & 0.02 & 0.04 & 0.31 & 0.52 & 0.11 & 0.01 \\ Detection Rate & 0.02 & 0.04 & 0.31 & 0.52 & 0.11 & 0.01 \\ Detection Prevalence & 0.02 & 0.04 & 0.31 & 0.52 & 0.11 & 0.01 \\ Balanced Accuracy & 1 & 1 & 1 & 1 & 1 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Class distribution summary for SVM model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Performance Metrics** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline Sensitivity & 0.98 & 0.93 & 0.96 & 0.96 & 0.93 & 0.94 \\ Specificity & 1.00 & 1.00 & 0.98 & 0.96 & 0.99 & 1.00 \\ PPV & 0.95 & 0.96 & 0.95 & 0.96 & 0.94 & 0.83 \\ NPV & 1.00 & 1.00 & 0.98 & 0.96 & 0.99 & 1.00 \\ Prevalence & 0.02 & 0.04 & 0.31 & 0.52 & 0.11 & 0.01 \\ Detection Rate & 0.02 & 0.03 & 0.30 & 0.50 & 0.10 & 0.01 \\ Detection Prevalence & 0.02 & 0.04 & 0.31 & 0.51 & 0.11 & 0.01 \\ Balanced Accuracy & 0.99 & 0.97 & 0.97 & 0.96 & 0.96 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Class distribution summary for DT model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & **Actual** & & \\ \cline{2-5}
**Prediction** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline
2 & 837 & 0 & 0 & 0 & 0 & 0 \\
3 & 2 & 1662 & 0 & 0 & 0 & 0 \\
4 & 0 & 3 & 13,812 & 6 & 0 & 0 \\
5 & 0 & 0 & 10 & 22,893 & 7 & 0 \\
6 & 0 & 0 & 0 & 15 & 4848 & 9 \\
7 & 0 & 0 & 0 & 0 & 21 & 292 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Support vector machine confusion matrix.  
all soil types, indicating its reliability in predicting the samples that do not belong to a particular soil class. Overall, the model performs exceptionally well on the dataset in terms of multiple performance metrics.