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Rforestry: Random Forests, Linear Trees, and Gradient Boosting for Inference and Interpretability
Provides fast implementations of Honest Random Forests, Gradient Boosting, and Linear Random Forests, with an emphasis on inference and interpretability. Additionally contains methods for variable importance, out-of-bag prediction, regression monotonicity, and several methods for missing data imputation. Soren R. Kunzel, Theo F. Saarinen, Edward W. Liu, Jasjeet S. Sekhon (2019) <arXiv:1906.06463>.
Getting started
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Package details |
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| Author | Sören Künzel [aut], Theo Saarinen [aut, cre], Simon Walter [aut], Sam Antonyan [aut], Edward Liu [aut], Allen Tang [aut], Jasjeet Sekhon [aut] |
| Maintainer | Theo Saarinen <theo_s@berkeley.edu> |
| License | GPL (>= 3) |
| Version | 0.10.0 |
| URL | https://github.com/forestry-labs/Rforestry |
| Package repository | View on CRAN |
| Installation |
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