# An Advanced Soil Classification Method Employing the Random Forest Technique in Machine Learning
## 6 Conclusions  
This study uses an ML model, particularly RF, to classify soil. Through this innovative approach, the developed RF model effectively classifies soil. The main findings of this study can be summarized as follows:  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Soil Sample**} & **Coefficient of Curvature** & **Coefficient of Uniformity** & **Plasticity Index (PI)** & **Organic Soil** & **Liquid Limit** & **Percentage** & **Percentage** & **Soil Classification** \\  & **(Cc)** & **(Cu)** & **Soil** & **or Inorganic** & **Passing** & **Passing** & **No. 4 Sieve** & **No. 200 Sieve** & **Co**-SM \\ \hline  
63 & 6.99 & SP-SM \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **Data** & **Predicted Results** \\ \hline
1 & All data included & 46 of 47 samples are correctly predicted \\ \hline
2 & Cc is neglected & 44 of 47 samples are correctly predicted \\ \hline
3 & Cu is neglected & 45 of 47 samples are correctly predicted \\ \hline \hline \end{tabular}
\end{table}
Table 7: Predicted results for 47 soil samples.  
1. This study first employs the OOB predictor within the RF algorithm to evaluate variable importance. The analysis reveals that Factor 5 (LL) and Factor 7 (percentage passing No. 200 sieve) possess the highest importance scores. Since the key components of the USCS are grain size distribution and Afterberg limits, with Factor 5 (LL) and Factor 7 (percentage passing No. 200 sieve) being crucial at the first and second levels of classification, the results imply that they significantly influence the classification outcome.
2. Through cross-validation, the performance of the RF model is rigorously evaluated, demonstrating its capability for accurate soil classification. The indices illustrate that the VAF, R\({}^{2}\), PI, NS, WI, AIC, RMSE, MAE, and WMAPE values are 100, 1, 2, 1, 1, \(-\)2.23 \(\times\) 10\({}^{7}\), 7.96 \(\times\) 10\({}^{-4}\), 6.48 \(\times\) 10\({}^{-6}\), and 1.16 \(\times\) 10\({}^{-5}\), respectively. These results highlight the robustness and reliability of the proposed RF model in effectively discerning soil characteristics.
3. Furthermore, this study examines the impact of missing input factors on the RF model's ability to classify soil characteristics. The analysis reveals that this trend aligns with the findings from the OOB importance analysis. The results demonstrate that the USCS is already optimized regarding the laboratory work. Since Factor 1 (Cc) and Factor 2 (Cu) are calculated from the grain size distribution, the proposed RF model still achieved accurate soil classification despite the omission of these factors.  
Methodology, investigation and writing, C.-Y.L.; conceptualization and supervision, C.-Y.K.; validation and visualization, T.-Y.W.; data curation, Y.-C.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: Data is contained within the article. The authors declare no conflicts of interest.