# Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining
## 3 Results
### Model Training and Evaluation  
In the training of the ILB model, an initial model (ILB0) is established using the first case from the training set, and the accuracy and AUC of the model are assessed using the test set. Subsequently, incremental training is conducted based on the initial model. For each new case, incremental training is performed, and the model's accuracy and AUC are re-evaluated. After completing training for all cases in the training set and completing one training round, the changes in accuracy and AUC with training iterations are recorded. The order of input cases significantly affects the training results of the ILB model. However, the data collected in this study do not include temporal information. Conducting training for just one round without considering data input order would lead to an inability to discern trends in the model's performance. Hence, the order of cases in Dataset I's training set is randomized before the next training round. A total of 10 training rounds are planned, resulting in 10 sets of accuracy and AUC values varying with training iterations. These 10 values are averaged to determine the ILB model's average accuracy and AUC with varying training iterations. A point-line plot is created based on these average values, as depicted in Figure 4.  
Figure 3: Correlation matrix of variables for all slope cases. In the training of batch learning models, the dataset is initially standardized, followed by the utilization of the grid search method to identify the optimal parameter combination for each algorithm. The training dataset is partitioned using fivefold cross-validation to facilitate sampling. The range of hyperparameters for each model is detailed in Table 4. To facilitate a comparison with the ILB model, the training dataset is segmented into 10 equal groups, each with 13 cases. Initially, batch training is conducted using the first group of cases, with model performance evaluated based on accuracy and AUC metrics. Subsequently, the first two groups are reintroduced into the batch learning models for retraining, and model performance is tested. This process is repeated, incrementally adding case groups until all 10 are included, completing a full training round. Given that there were four batch learning models, 40 training sessions were conducted in each round. To ensure consistency in experimental conditions with the ILB model, 10 training rounds were also implemented for the batch learning models. Before each training round, the order of cases within Dataset I's training set was randomized, requiring 400 batch training sessions  
Figure 4: Performance curves of each classification model. across all models. Each batch training session involved re-optimization to ascertain the current optimal model. Following 10 training rounds, the accuracy and AUC of the four batch learning models were evaluated using the test set and then arithmetically averaged to establish the mean performance metrics for each model. Finally, point-line plots depicting the average accuracy and AUC of the four batch learning models as a function of the number of input cases were generated, as shown in Figure 4.  
Figure 4 illustrates a gradual improvement in accuracy and AUC for each model as the number of cases increases. The ILB model outperformed the four batch learning models in classification. The accuracy of the ILB model surpassed that of the GBM, SVM, and MLP batch-learning models and was on par with that of the RF model. Below 65 cases, ILB outperformed RF. Beyond 65 cases, both models exhibited similar accuracy. In 13 and 26 cases, ILB notably outperformed the other four models in accuracy. By 38 cases, ILB achieved 90% of its final accuracy. This demonstrates ILBs superior performance on small datasets. Except for RF, the accuracy curves of the other three batch learning models exhibited noticeable fluctuations, whereas the accuracy curve of the ILB model remained stable, indicating a consistent performance of the ILB model. In terms of AUC, the ILB model performed well.