## 3 Results
### SHAP Explanations  
This section presents and interprets SHAP summary graphs for each machine learning model employed in this study. The plots in Figure 5 visualise the magnitude and direction of the contribution of features to individual predictions, i.e., how individual features affect predictions, offering insights into the behaviour and interpretability of the model. Specifically, positive SHAP values indicate features that increase the predicted outcome, \begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Train (s)** & **Test (s)** \\ \hline k-NN & 0.1394 & 0.4714 \\ RF & 9.4640 & 0.0818 \\ NN & 24.0860 & 0.0656 \\ DT & 1.1772 & 0.0068 \\ XGBoost & 49.5807 & 0.2582 \\ LightGBM & 0.1927 & 0.0200 \\ SVM & 3642.6518 & 39.0189 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Training and testing time (in seconds) for each model. Figure 4: Model comparison based on mean \(R^{2}\) values with 95% confidence intervals shown as error bars. The red dashed line represents the baseline model performance (90.28%). while negative values represent decreasing effects. The horizontal spread of SHAP values illustrates how the impact of the feature varies between instances, and the associated colour gradient, from red (high feature values) to blue (low feature values), helps to interpret the influence of individual observations. The SHAP summary plot for the Decision Tree model highlights that _Days from Dry Dock_ and _Draft_ exhibit the highest SHAP values, signifying their dominant role in shaping the model's output. The k-Nearest Neighbors model reveals that _Days from Dry Dock_ and _Days from Delivery_ are the most influential variables. Although k-NN is a non-parametric model, the SHAP values offer a valuable explanation of how feature values shape predictions. The plot shows that the impact of each feature varies considerably between samples, revealing localised importance and helping to address the interpretability challenges commonly associated with distance-based models. For the LightGBM model, SHAP analysis once again highlights _Days from Dry Dock_ and _Draft_ as the most critical features. Sharp concentrations of SHAP values in certain areas of the plot suggest that the model has learned specific feature interactions or rule-like behaviours. Compared to simpler models, LightGBM displays a wider range of SHAP distributions, reflecting the nuanced and data-driven decision boundaries learned during training. In the case of the neural network model, SHAP values emphasise the significant contributions of _GPS speed_ and _Wave Height_. The wide dispersion of SHAP values across samples underscores the model's ability to capture complex, non-linear interactions. In addition, the colour gradients within the graph reveal how high the feature values (such as for _Days from Dry Dock_) consistently influence the model in a particular direction, further illuminating the behaviour of the model. The Random Forest model exhibits SHAP patterns characteristic of tree-based ensembles. Notably, _Draft_ and _Days from Dry Dock_ emerge as key predictors. The clustered appearance of SHAP values is likely due to the model's discrete decision thresholds. This structure results in a broad distribution of feature contributions across the dataset, revealing considerable variability in how the model assigns importance to different features depending on the input. XGBoost, as a gradient-boosted ensemble, shows a refined and compact spread of SHAP values. The model heavily relies on _Days from Dry Dock_ and _Wave Height_, both of which display broad and well-defined SHAP distributions. High values of these features (indicated in red) frequently correspond to positive SHAP contributions, suggesting their consistent influence in driving the prediction upward. The sharply defined value regions in the SHAP plot reflect the model's finely tuned learning process. For the SVM model, the SHAP plot reveals a narrower range of feature impacts compared to tree-based models.