Table 1 Accuracy assessment of ML models using multiple evaluation metrics
From: Simulating flood risk in Tampa Bay using a machine learning driven approach
Metrics | DT | SVM | AdaBoost | XGBoost | RF |
|---|---|---|---|---|---|
Accuracy | 0.93 | 0.92 | 0.95 | 0.96 | 0.96 |
Precision | 0.93 | 0.87 | 0.92 | 0.94 | 0.93 |
Recall | 0.93 | 0.98 | 0.98 | 0.98 | 0.99 |
F-1 score | 0.93 | 0.92 | 0.95 | 0.96 | 0.96 |
Kappa score | 0.87 | 0.83 | 0.90 | 0.93 | 0.92 |
Jaccard score | 0.87 | 0.85 | 0.90 | 0.93 | 0.92 |
