Pure interaction effects unseen by Random Forests
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908475386232832 |
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| author | Blum, Ricardo Hiabu, Munir Mammen, Enno Meyer, Joseph Theo |
| author_facet | Blum, Ricardo Hiabu, Munir Mammen, Enno Meyer, Joseph Theo |
| contents | Random Forests are widely claimed to capture interactions well. However, some simple examples suggest that they perform poorly in the presence of certain pure interactions that the conventional CART criterion struggles to capture during tree construction. Motivated from this, it is argued that simple alternative partitioning schemes used in the tree growing procedure can enhance identification of these interactions. In a simulation study these variants are compared to conventional Random Forests and Extremely Randomized Trees. The results validate that the modifications considered enhance the model's fitting ability in scenarios where pure interactions play a crucial role. Finally, the methods are applied to real datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_15500 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Pure interaction effects unseen by Random Forests Blum, Ricardo Hiabu, Munir Mammen, Enno Meyer, Joseph Theo Machine Learning Random Forests are widely claimed to capture interactions well. However, some simple examples suggest that they perform poorly in the presence of certain pure interactions that the conventional CART criterion struggles to capture during tree construction. Motivated from this, it is argued that simple alternative partitioning schemes used in the tree growing procedure can enhance identification of these interactions. In a simulation study these variants are compared to conventional Random Forests and Extremely Randomized Trees. The results validate that the modifications considered enhance the model's fitting ability in scenarios where pure interactions play a crucial role. Finally, the methods are applied to real datasets. |
| title | Pure interaction effects unseen by Random Forests |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.15500 |