# Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining
## 4 Discussion  
### Variable Importance and Partial Dependence Plots  
The significance of each feature was determined by ranking their importance and fitting the data with logistic regression, as shown in Figure 6. In logistic regression, the larger the absolute value of a coefficient, the greater the importance of the corresponding feature. In Figure 6, the angle of internal friction emerges as the most crucial feature, followed by slope angle and pore water ratio. Conversely, slope height, cohesion, and unit weight are less important. These data underscore the impact of each selected feature on prediction results, validating the rationale behind feature selection in this study. [PERSON], et al. [51] ranked the factors that affect slope stability and found that overall slope angle and angle of internal friction are the most important indicators, while the importance of slope height is low. Figure 5: Generalization ability of each classification model. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Algorithm** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1-Score** \\ \hline ILB & 0.877 & 0.835 & 0.881 & 0.949 & 0.914 \\ RF & 0.884 & 0.841 & 0.878 & 0.923 & 0.900 \\ GBM & 0.807 & 0.784 & 0.872 & 0.872 & 0.872 \\ MLP & 0.854 & 0.849 & 0.873 & 0.923 & 0.893 \\ SVM & 0.877 & 0.835 & 0.881 & 0.949 & 0.914 \\ \hline \end{tabular}
\end{table} 
Table 5: Model performance indicators. [PERSON], et al. [52] listed unit weight as the least important factor in the study of slope stability. Consequently, enhancing slope stability entails improving the angle of internal friction and reducing the overall slope angle. Employing engineering techniques to enhance slope soil's internal friction angle and decrease the overall slope angle is an effective strategy for bolstering slope performance. This study investigates the partial dependence of each variable in the ILB model by generating partial dependence plots (PDPs) for each variable, as depicted in Figures 7 and 8. A PDP illustrates the marginal impact of one or two features on the prediction outcomes of machine learning models [53]. PDP elucidates the intricate relationship between targets and features, aiding in clarifying the association between specific variables and prediction outcomes. The curve showcases the partial dependency between features and prediction results in PDP. Figure 6: Permutation feature importance plot generated by the ILB model. Figure 7: Partial dependence plots of the influencing variables in the ILB model. The PDP curves in Figure 7 exhibit fluctuations, indicating the influence of the six selected variables on classification outcomes. The lower segments of the curves are violin plots of variable distributions. Ups and downs in the curves signify a variable's impact on model predictions, while parallelism to the horizontal axis suggests negligible influence. Among the variables, unit weight (\(\gamma\)), angle of internal friction (\(\varphi\)), height (\(H\)), and slope angle (\(\beta\)) curves demonstrate an increased likelihood of stability with larger values. Cohesion (C) initially enhances stability probability but sharply declines afterward, potentially due to limited data beyond a certain point. The pore water ratio (\(ru\)) curve forms an inverted U-shape, showing initial stability improvement with \(ru\) until a peak, followed by a subsequent decline beyond a specific threshold. Two-dimensional PDPs were created to explore the relationship between pairs of variables. Each pair of variables generated a partial plot, collectively forming a partial dependence matrix illustrated in Figure 8. In this matrix, numbers ranging from 0 to 1 represent slope stability, with higher numbers indicating greater stability. The colors in the matrix help us quickly assess slope stability: yellow areas represent regions with higher stability, while purple areas indicate lower stability.