Evaluating Road Crash Severity Prediction with Balanced Ensemble Models
This study evaluates the performance of an ensemble of five ML models (Random Forest, XGBoost, AdaBoost, LightGBM and CatBoost) on crash data from New South Wales, Australia. The model is evaluated based on ROC-AUC score, with a result of 0.68, indicating a moderate level of predictive accuracy. Fea...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Findings Press
2024-04-01
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| Series: | Findings |
| Online Access: | https://doi.org/10.32866/001c.116820 |
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