Research Article
An Alternative Method for Traffic Accident Severity Prediction: Using Deep Forests Algorithm
Table 4
The predictive performance of different accident categories.
| ā | Recall | False alarm rates | F1 score | ROC | 1 | 2 | 3 | 1 | 2 | 3 | 1 | 2 | 3 | 1 | 2 | 3 |
| Deep Forests | 0.93 | 0.82 | 1.00 | 0.17 | 0.09 | 0.01 | 0.88 | 0.86 | 1.00 | 0.91 | 0.88 | 1.00 | RFs | 0.91 | 0.77 | 1.00 | 0.19 | 0.10 | 0.01 | 0.86 | 0.83 | 1.00 | 0.90 | 0.86 | 1.00 | XGboost | 0.83 | 0.66 | 1.00 | 0.27 | 0.20 | 0.02 | 0.78 | 0.72 | 0.99 | 0.84 | 0.79 | 0.99 | LightGBM | 0.84 | 0.63 | 1.00 | 0.29 | 0.20 | 0.02 | 0.77 | 0.71 | 0.99 | 0.84 | 0.78 | 1.00 | Decision tree | 0.68 | 0.76 | 1.00 | 0.23 | 0.28 | 0.05 | 0.77 | 0.72 | 0.95 | 0.78 | 0.80 | 0.98 | KNN | 0.66 | 0.64 | 1.00 | 0.32 | 0.33 | 0.07 | 0.67 | 0.66 | 0.97 | 0.75 | 0.75 | 0.98 | DNN | 0.80 | 0.07 | 0.70 | 0.52 | 0.51 | 0.38 | 0.60 | 0.13 | 0.66 | 0.56 | 0.43 | 0.56 |
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