# Interpretability Study of Gradient Information in Individual Travel Prediction
## 4 Experiment
### Feature Impact Analysis
#### 4.4.2 Comparison of Multi-Feature Masking Policies  
The single-feature masking experiment preliminarily verified the effectiveness of gradient information in indicating feature importance. To gain a more comprehensive understanding of the contribution of each feature to the performance of the machine learning model, we further designed a multi-feature masking experiment. In the experiment, we ranked the features according to the gradient magnitude and adopted three different masking strategies: random-order masking, descending-order masking, and ascending order-masking. By observing the changes in model performance under different strategies, we can gain in-depth insights into the interactions between features and their combined impact on model predictions. Under the random-order masking strategy, we randomly selected a certain number of features for masking, which does not take into account the gradient information of the features. Therefore, it can serve as a baseline experiment to compare the effects of the other  
Figure 6: Change of PA value of model after masking single-feature at different positions. two strategies. There are many similarities between the different sorting results for the three different sequence lengths as shown in Figure 7:  
The descending-order masking strategy ranks and masks features in descending order of gradient values, forming different numbers of feature subsets until all features are masked. According to our hypothesis, these features contribute significantly to the model's predictions, so masking them would have a noticeable impact on model performance. The experimental results confirmed this hypothesis. In the descending order strategy, with a very small subset of features masked, the PA value showed a rapid upward trend. This indicates that these prioritized masked feature combinations play a \"core\" role in the model, providing strong information for predictions, such as the commuting peak on weekdays or high-frequency travel patterns between specific stations. In the three different lengths, after a sharp increase, the PA value experienced a brief decline and then rose gently. This may be due to the model starting to adjust its weight and dependencies, shifting to using the remaining features for predictions. Although these features may not be as important as the initially masked ones, they still contain a certain amount of information that can help the model maintain a certain level of prediction performance. The changes in JS divergence values support this inference. The JS values in descending order are relatively high, indicating significant changes in the output distribution, which proves that the masked feature combinations with larger gradients have a greater impact on model performance. Contrary to the descending order strategy, the ascending order masking strategy prioritizes masking features with smaller gradient values. The experimental results show that under this strategy, a larger feature masking subset is needed for the model performance to decline. One possible speculation is that the masking of non-important features in the early stage has a minor impact on model prediction performance. The JS distribution is lower because the masking of non-important features in the early stage has a low impact on model prediction performance. However, when the feature subset includes high-gradient  
Figure 7: **(a)** Line graph showing PA values after masking; **(b)** box plot presentation of JS values after masking. features, the model's JS divergence value will change dramatically, resulting in more outliers appearing above the box plot. This indicates that although low-gradient features have a limited impact on the model's immediate predictions, they still support the model's overall performance to a certain extent. Under the random masking strategy, the model's prediction performance declines gradually, but at a relatively gentle pace.