Table 1 Performance of selected algorithms (%)
From: Classifying bridges for the risk of fire hazard via competitive machine learning
| AUC Training | AUC Testing | No damage | Damage | Precision (%) | ||
|---|---|---|---|---|---|---|
| RF | 73% | 70% | No damage | 8/39/12 | 3/16/2 | 11/55/14 |
| Damage | 0/11/4 | 8/29/5 | 8/40/9 | |||
| Recall (%) | 8/50/16 | 11/45/7 | ||||
| SVM | 72% | 68% | No damage | 7/37/4 | 4/18/10 | 11/55/14 |
| Damage | 1/11/0 | 7/29/9 | 8/40/9 | |||
| Recall (%) | 8/48/4 | 11/47/19 | ||||
| GAM | 71% | 64% | No damage | 6/23/12 | 5/32/2 | 11/55/14 |
| Damage | 0/6/4 | 8/34/5 | 8/40/9 | |||
| Recall (%) | 6/29/16 | 13/66/7 |