## 4 Discussion  
In outage risk modeling, assessing the performance of different machine learning models is crucial to determining the model with the best predictive power. Many recent outage modeling studies have utilized various machine learning techniques to predict the number of outages or estimate the probability of outage risk. These methods include logistic regression, classification and regression trees, decision trees, multivariate adaptive regression splines, artificial neural networks, naive Bayes regression, random forests, boosting, and an ensemble model of boosting and random forests [5; 11; 18; 46]. Non-parametric machine learning algorithms have recently gained more attention than parametric models in outage modeling problems [8] due to their ability to capture complex data relationships and make fewer assumptions about the data distribution [47]. There is a lack of literature demonstrating how support vector machines, k-Nearest Neighbor (k-NN), and extreme gradient boosting (XGBoost) can provide inferences on estimating the probability of tree failure in addition to decision trees (DTs) and random forests (RFs). Despite differences in the machine learning algorithms used, we can compare our findings with previous studies on the applicability of different machine learning techniques for predicting the probability of tree-related outage risk during storms. The quality of the prediction is crucial, especially when dealing with issues related to electric \begin{table}
\begin{tabular}{c c c} \hline \hline
**ML Algorithm** & **Computational Time for HO (s)** &
\begin{tabular}{c} **Mean Computational Time** \\ **for MPE (s)** \\ \end{tabular} \\ \hline DT & 3.93 & 0.41 \\ k-NN & 12.74 & 4.41 \\ SVM & 224.91 & 112.26 \\ RF & 2825.82 & 66.79 \\ XGBoost & 9438.54 & 73.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Computational runtime required for CT for hyperparameter optimization and model performance evaluation (MPE). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ML Algorithm** & **AUC-ROC** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Avg Ranking** & **Overall Ranking** \\ \hline DT & 5 & 5 & 3 & 4 & 4 & 4.2 & 5 \\ k-NN & 3 & 2 & 3 & 1 & 4 & 2.6 & 4 \\ SVM & 1 & 2 & 3 & 1 & 2 & 1.8 & 2 \\ RF & 1 & 1 & 1 & 3 & 1 & 1.4 & 1 \\ XGBoost & 3 & 2 & 1 & 4 & 2 & 2.4 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Selecting the best-performing model using the ranking scheme based on different performance metrics. power lines. Confidence intervals (CIs) have been utilized in many machine learning works to quantify the reliability or uncertainty of machine learning interpretations [48; 49]. It is important to discuss how five different ML models performed since the performance of some of the ML models is quite similar for some performance metrics. Also, even a 0.1% improvement is considered significant given the scale of the study, encompassing approximately 49,000 device exposure zones (DEZs), and the substantial impact that certain DEZ locations have on the economic and power security aspects of the electric grid. Based on the model performance evaluation and ranking scheme, the random forest (RF) algorithm emerged as the best ML algorithm according to the metrics of the AUC-ROC, accuracy, precision, and the F1-score in assessing the tree-related outage probability on distribution power lines, implying superior performance compared to the other algorithms. The RF and the SVM showed similar results for the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metric. This implies that both models have similar potential to measure the percentage of correct predictions and the ability to separate true classes from negative classes. k-NN and the SVM outperformed the RF when assessing the recall (sensitivity). Recall and precision have important implications for the operational value of machine learning in risk assessment.