## 3 Results
### Surrounding Rock Excavatability Identification Based on Deep Forest
#### 3.2.5 Comparison of Different Models  
Other machine learning models can also solve the classification identification problem of SRE grades. Therefore, the same dataset was used for SRE classification using four well-developed machine learning algorithms: decision tree (DT), RF, support vector classifier (SVC), and deep neural network (DNN). These four algorithms were validated by hyperparameter optimization with 5-fold cross-validation. Table 10 shows the hyperparameter ranges and optimal values, and Figure 30 shows the confusion matrices. Table 11 shows the value of the main hyperparameters of DT, RF, SVC, and DNN. Based on the confusion matrices of individual algorithms, the performances of individual algorithms were calculated and compared with DF, as shown in Figure 31.  
Figure 31 shows that among the five algorithms, DF achieved the best prediction with an accuracy of 0.9633 and an F1 score of 0.9581. In contrast, the DNN algorithm had the worst performance, with \"precision/F1 scores\" of \"0.9080/0.8883\". This is because the random features and samples for the DF decision tree in dealing with nonlinear problems reduced the variance and overfitting of the model. DNN may be affected by problems such as local minima or gradient disappearance when dealing with nonlinear problems, thus affecting model performance. DF improved the stability and robustness of the model via integrated learning and performed better for data samples of different sizes. In addition, RF was second only to DF in terms of the model performance, with \"accuracy/F1 scores\" of \"0.9576/0.9517\". Although DF and RF are both characterized as integrated learning algorithms, DF has a deeper integration of decision trees than RF, with a stronger generalization ability. Therefore, it is feasible to use DF as an intelligent identification method for SRE classification perception. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Key Parameters** & **Selected Values** & **Optimal Values** \\ \hline \multirow{4}{*}{DT} & _Max depth_ & [10, 15, 20, 25, 30] & 20 \\  & _Min samples leaf_ & [1, 2, 3, 4, 5] & 1 \\  & _Min samples split_ & [2, 3, 4, 5, 6] & 2 \\ \hline \multirow{4}{*}{RF} & _nEstimators_ & [50, 100, 150, 200, 300] & 150 \\  & _Max depth_ & [10, 15, 20, 25, 30] & 25 \\  & _Min samples split_ & [2, 3, 4] & 2 \\  & _Min samples leaf_ & [1, 2, 3, 5] & 1 \\  & _Max features_ & [auto,sqrt,log2] & auto \\ \hline \multirow{2}{*}{SVC} & _kernel_ & [Linear,poly,rbf,sigmoid] & poly \\  & C & [0.25, 0.5, 1, 2, 4, 8, 16] & 16 \\ \hline \multirow{4}{*}{DNN} & _Hidden layer sizes_ & [(10),) (20,), (30), (10, 10), (20, 10), (30, 20)] & (30, 20) \\  & _activation_ & [relu,tanh.logistic] & relu \\  & _learning_rate_ & [constant,invscaling_adaptive] & adaptive \\  & _alpha_ & [0.001, 0.01, 0.1] & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Grid search and optimal parameters of classifiers. In addition, Figure 31 shows that all five models achieved good results overall, with accuracies above 0.9080. This was related to model performance, hyperparameter optimization, and data samples. Data sample labels are an important part of the dataset and significantly affect model training. This dataset label was benchmarked with the SRE classification system proposed in this paper. Therefore, it is more rational to use this system for rock mass excavatability classification. This can provide a reference for TBM tunnels of similar lithology. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameters** & **Values** \\ \hline \multirow{8}{*}{DT} & _Max depth_ & 20 \\  & _Min samples leaf_ & 1 \\  & _Min samples split_ & 2 \\ \cline{1-1}  & _Max features_ & None \\ \cline{1-1}  & _Criterion_ & gini \\ \cline{1-1}  & _Class weight_ & None \\ \cline{1-1}  & _Max leaf nodes_ & None \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Hyperparameter values of DT, RF, SVC, and DNN. Figure 30: The confusion matrices of the four models on the test set: (**a**) DT; (**b**) RF; (**c**) SVC; (**d**) DNN. **Table 11.**_Cont._