## 4 Results and Discussion
### Global Explanations of Prediction Model Based on SHAP  
#### 4.3.1 Feature Importance  
Feature importance is a measure of the extent to which the features of each explanatory variable affect its predicted value. This analysis clarified the process and basis of the prediction results, allowing for an easy technical explanation. The evaluation of explanatory variables for the entire instance is distinguished as a global explanation. In this study, we evaluated the impacts of explanatory variables on liquefaction ground subsidence. In the data analysis, stratified sampling was performed for each geomorphological classification. This is because data imbalances can occur during random sampling. Geomorphological classifications were added to the explanatory variables through dummy-variable transformation using one-hot encoding. An overrepresentation of the same geomorphological classification from random sampling cab biases those results. Stratified sampling mitigates categorical data biases and facilitates precise feature importance analysis by ensuring proportional representation across classes. For stratified sampling, 30 data points for each relevant geomorphological classification were randomly extracted from the training, evaluation, and test data. As 20 points were extracted for each category, applying this to the 25 categories resulted in a sample size of 500. The average absolute SHAP value is an indicator of the feature importance obtained by taking the absolute SHAP value for each instance and averaging it over the number of samples. Notably, the average absolute SHAP value does not directly indicate an increase or decrease in the relationship with the predicted value but visualizes the absolute amount of information contained in the explanatory variables. For example, ICE (individual conditional expectation) and PD (partial dependence) allow us to visualize whether the predictions change by varying the values of the features. However, the complexity of the data makes it difficult to interpret and explain the model, and the impact on the predicted value cannot be visualized, so different evaluation merits are used depending on what is being evaluated. Figures 7-9 show the SHAP and average absolute SHAP values for each instance of the 44 explanatory variables used in this study. They also show the GIS data, borehole data, and explanatory variables related to geomorphological classification, respectively, all in descending order. In addition, the distribution of the features and the SHAP values for each explanatory variable in each instance are shown. Although the average absolute SHAP value evaluates the impact of the explanatory variables as a single value of feature importance, it does not provide a view of the liquefaction ground subsidence trend for each instance. Therefore, by using summary plots to represent visually each instance's features and SHAP values on a plane, the liquefaction ground subsidence factors were interpreted. Figure 8: The average absolute SHAP values for the 11 explanatory variables in borehole data and the SHAP values for each instance: (**a**) Feature importance plot; (**b**) summary plot. Figure 7: The average absolute SHAP values for the eight explanatory variables in GIS data and the SHAP values for each instance: (**a**) Feature importance plot; (**b**) summary plot. Figure 7 shows the contribution of each explanatory variable to the predicted value in the GIS data. The variable with the most significant impact on the amount of ground subsidence due to liquefaction was the JMA seismic intensity at the engineering bedrock (Ia_min_dI). Liquefaction phenomena depend on the intensity of seismic motion and occur because of shear stress within the ground. Examining the factor plot for the JMA seismic intensity on the engineering bedrock (Ia_min_dI), it can be seen that as the explanatory variable for the base seismic intensity increased, the impact on the predicted value (SHAP value) also increased, indicating a positive correlation.