# Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining
## 3 Results
### Model Training and Evaluation
The AUC of the ILB model consistently surpassed that of GBM across the entire range of variations. Before reaching the maximum number of cases, the AUCs of SVM and MLP were lower than those of the ILB model. Only when the number of cases reached 133 did their AUC values surpass those of the ILB model slightly. The AUC of RF was lower than that of the ILB model until 78 cases and then slightly exceeded ILB. The AUC curve of the ILB model exhibited less fluctuation than other models. Initially steep, the AUC curve of the ILB model gradually leveled off after 20 cases. The ILB model showed a faster AUC growth rate, reaching 90% of its final AUC when the number of cases reached 15, with a smooth curve indicating stable performance. Overall, the ILB model's final classification effectiveness was comparable to that of batch learning models, showing superior classification performance. In addition to accuracy and AUC, this study also evaluated the F1 score, precision, and recall of the ILB model and four batch learning models, as shown in Table 5, from which we can see that the ILB model's performance indicators were all around 0.8, indicating a well-balanced overall performance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Hyper-Parameters** & **Value Range** \\ \hline \multirow{4}{*}{RF} & n\_estiamtors & \{100, 200, 300, 400, 500\} \\  & max\_depth & \{1, 3, 5, 7, 9\} \\  & min\_samples\_leaf & \{1, 2, 4, 6\} \\  & min\_samples\_split & \{2, 3\} \\  & learning\_rate & \{0.001, 0.01, 0.05, 0.1\} \\  & max\_depth & \{3, 7, 11, 15\} \\ GBM & min\_samples\_leaf & \{1, 2, 5\} \\  & n\_estimors & \{100, 300, 500, 700, 900\} \\  & activation & \{identity, logistic, tanh, relu\} \\  & solver & \{adam, sgd, lbfgs\} \\ MLP & batch\_size & \{32, 64, 128, 256\} \\  & learning\_rate\_init & \{0.001, 0.01, 0.1, 1\} \\  & C & \{0.1, 1, 2, 4, 8, 16, 32, 64, 128\} \\ SVM & gamma & \{0.1, 0.12, 0.14, 0.16, 0.18, 0.2, 1\} \\  & kernal & \{linear, poly, rbf, sigmoid\} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Tuning parameters for each model. To assess the models' generalization capabilities, Dataset II was employed to evaluate the accuracy and AUC of the final models trained with all cases in the training set. The test results of both the ILB and four batch learning models are depicted in Figure 5, where the horizontal axis represents the models' accuracy on Dataset II, and the vertical axis represents the corresponding AUC. The models that exhibited numerical points closer to the upper right corner of the chart demonstrated superior generalization abilities. Figure 5 shows the ILB model's data point in the upper right corner, indicating its optimal performance and superior generalization on Dataset II. Following closely was RF, boasting an accuracy and AUC exceeding 0.8, slightly trailing the ILB model but still show-casing robust capabilities. Conversely, MLP, SVM, and GBM are located in the lower-left corner of the chart, displaying lower accuracy and AUC values below 0.75, suggesting that their generalization abilities are poor.