Conformal prediction for frequency-severity modeling
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
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| _version_ | 1866915580148187136 |
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| author | Graziadei, Helton F., Paulo C. Marques de Melo, Eduardo F. L. Targino, Rodrigo S. |
| author_facet | Graziadei, Helton F., Paulo C. Marques de Melo, Eduardo F. L. Targino, Rodrigo S. |
| contents | We present a model-agnostic framework for the construction of prediction intervals of insurance claims, with finite sample statistical guarantees, extending the technique of split conformal prediction to the domain of two-stage frequency-severity modeling. The framework effectiveness is showcased with simulated and real datasets using classical parametric models and contemporary machine learning methods. When the underlying severity model is a random forest, we extend the two-stage split conformal prediction algorithm, showing how the out-of-bag mechanism can be leveraged to eliminate the need for a calibration set in the conformal procedure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_13124 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Conformal prediction for frequency-severity modeling Graziadei, Helton F., Paulo C. Marques de Melo, Eduardo F. L. Targino, Rodrigo S. Methodology Machine Learning We present a model-agnostic framework for the construction of prediction intervals of insurance claims, with finite sample statistical guarantees, extending the technique of split conformal prediction to the domain of two-stage frequency-severity modeling. The framework effectiveness is showcased with simulated and real datasets using classical parametric models and contemporary machine learning methods. When the underlying severity model is a random forest, we extend the two-stage split conformal prediction algorithm, showing how the out-of-bag mechanism can be leveraged to eliminate the need for a calibration set in the conformal procedure. |
| title | Conformal prediction for frequency-severity modeling |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2307.13124 |