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Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection
Gradient Boosting Machines (GBM) are among the go-to algorithms on tabular data, which produce state-of-the-art results in many prediction tasks. Despite its popularity, the GBM framework suffers from a fundamental flaw in its base learners. Specifically, most implementations utilize decision trees...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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MDPI
2022
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140774/ https://www.ncbi.nlm.nih.gov/pubmed/35626570 http://dx.doi.org/10.3390/e24050687 |