Predictive performance of count regression models versus machine learning techniques: A comparative analysis using an automobile insurance claims frequency dataset.
Accurate forecasting of claim frequency in automobile insurance is essential for insurers to assess risks effectively and establish appropriate pricing policies. Traditional methods typically rely on a Poisson distribution for modeling claim counts; however, this approach can be inadequate due to fr...
| Published in: | PLoS ONE |
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| Main Author: | |
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-01-01
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| Online Access: | https://doi.org/10.1371/journal.pone.0314975 |