## 4 Results Analysis and Discussion
### Feature Importance Comparison  
Feature importance plays a critical role in machine learning, aiding in understanding the decision-making process of models, enhancing model performance, and enabling feature selection. In this section, we compare the feature importance across Decision Tree, Gradient Boosting Regressor, and Random Forest models to evaluate the contribution of each feature to the models. The training data includes nine features: initial position longitude, initial position latitude, berth longitude, berth latitude, ship length, ship width, ship draft, ship type, and time. Figure 10 illustrates the feature importance for the Decision Tree model, Gradient Boosting Regressor, and Random Forest model. As shown in Figure 10, the trends in feature importance across the three models are largely consistent. Initial position, berth position, ship length, and time are highly important in all models, while ship width, draft, and ship type have relatively lower importance. This aligns well with practical maritime operations, as a vessel's position at a given time is closely related to the berth and navigational channel entry/exit points. According to maritime regulations, different sized vessels have varying speed limits within the channel, which affects their speed differently. Additionally, vessels of different sizes tend to have different speeds when approaching or leaving the berth. Figures 11 and 12 compare the performance in terms of Mean Squared Error (MSE) and adjusted \(R^{2}\) under three scenarios: retaining all features, removing vessel width, draft, and ship type, and removing vessel length, width, draft, and ship type. Figure 10: Feature Importance Diagrams: (**a**) Decision Tree Feature Importance; (**b**) Gradient Boosting Regressor Feature Importance; (**c**) Random Forest Feature Importance. Figure 11 compares the MSE of DT, GBR, and RF models on both the training and testing datasets under scenarios of including all features and excluding specific features (B, DM, SHIPTYPE). For the four prediction targets (Longitude (LON), Latitude (LAT), SOG, and COG, the results show that the Random Forest regressor achieves the lowest MSE in all scenarios, particularly excelling on the testing set. This indicates that the Random Forest regressor delivers optimal performance across these four prediction tasks and possesses strong generalization capabilities. The Random Forest regressor performs excellently across different feature sets, demonstrating lower MSE when B, DM, and SHIPTYPE are excluded. However, Removing the L feature has had a multifaceted impact on model performance. In some cases, such as with SOG, removing the L feature effectively reduces the MSE on both the training and test sets, thus improving the model's generalization ability. However, in other scenarios, such as with LON, LAT, and COG, removing the L feature can actually increase the test set error. This is particularly evident with COG in the Random Forest model, where the MSE increases significantly, indicating that the L feature has a substantial impact on predicting COG. The performance stability of all models suggests that feature selection significantly impacts the models' predictive capabilities, but the Random Forest regressor is better able to adapt to these changes. Figure 12 shows the adjusted \(R^{2}\) values of the Decision Tree Regressor, Gradient Boosting Regressor, and Random Forest Regressor on the four prediction tasks (LON, LAT, SOC, COG), comparing their performance on the training and testing datasets. The figure compares three feature sets: including all features (All Features), excluding specific features (Except B, DM, SHIPTYPE), and excluding a broader set of features (Except B, L, DM, SHIPTYPE). In the Decision Tree model, the adjusted \(R^{2}\) values show almost no difference across the three feature sets, indicating that these features have minimal impact on the model's performance.