## 4 Discussion
A high recall score shows that the model can detect the majority of DEZs with outages while limiting the number of DEZs falsely predicted as having no outages (false negatives). Such false predictions may fail to identify some sensitive DEZs in the power grid and lead to serious consequences when trees fall onto power lines during severe weather events. In contrast, the RF and XGBoost models reported higher precision values compared to the other models, indicating that the SVM, k-NN, and the DT tend to falsely detect outages in DEZs (false positives) more frequently than the RF and XGBoost. This is important because utilities do not have to invest capital in DEZs that do not pose a risk to power lines. According to the ranking scheme based on the performance metrics and confidence interval values, the random forest (RF) was identified as the best machine learning model for assessing the probability of tree failure. Numerous previous studies have demonstrated the effectiveness of RFs in assessing the likelihood of tree failure [46] and predicting the number of power outages during storm events [50], highlighting their applicability in vegetation risk assessment. The DT was selected as the least performant model based on the AUC-ROC, accuracy values, and ranking scheme. DTs can perform poorly due to a variety of factors, such as the limitations of the pruning algorithms, and the algorithm can be seriously affected by the curse of dimensionality [51; 52]. In tree-related outage risk assessment, it is crucial to have a model that can accurately identify risk areas because misclassifications can result in missed opportunities for intervention to improve resilience, which could lead to higher outage risks in future storm conditions. Recording the computational runtimes of different machine learning (ML) models is important in data science. Computational runtime provides insights into algorithm optimization opportunities and resource planning. XGBoost and the RF consumed a comparatively higher runtime (in seconds) for hyperparameter optimization when compared to the other three models. The most probable reason is a large parameter space: XGBoost and RFs have a large parameter space for hyperparameter tuning. The computational runtime of hyperparameter optimization and model development may vary from several minutes to days depending on the scale of the data, the available computational resources, and the model complexity [53]. Moreover, the number of hyperparameters considered makes it time-consuming to search through all possible combinations of hyperparameters to find the optimal set. Since XGBoost and RFs are computationally intense algorithms, they can make hyperparameter optimization more time-consuming since the training process needs to be repeated multiple times to tune the hyperparameters. Additionally, certain types of hyperparameters and their values have a prompt effect on the execution time, such as the number of trees in RFs and XGBoost and the number of neighbors in k-NN [54]. The decision tree (DT) algorithm is comparatively simple, consisting of only one decision tree [22] and requiring fewer parameters. Therefore, it consumes less time compared to other complex algorithms. This study offers significant insights into vegetation risk assessment across Connecticut, utilizing a different dataset and employing various non-parametric machine learning models. The findings are particularly relevant for utility companies and arborists in regions with similar environmental conditions and vegetation dynamics as those in the northeastern United States. While this study was initially designed for Connecticut, the methodologies and principles can be adapted and extended to other regions. But it is important to recognize potential limitations, such as differences in vegetation types in different regions, which might need to be adjusted for the best results.