stackgbm: Stacked Gradient Boosting Machines
A minimalist implementation of model stacking by
Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1> for boosted tree models.
A classic, two-layer stacking model is implemented, where the first layer
generates features using gradient boosting trees, and the second layer
employs a logistic regression model that uses these features as inputs.
Utilities for training the base models and parameters tuning are provided,
allowing users to experiment with different ensemble configurations easily.
It aims to provide a simple and efficient way to combine multiple
gradient boosting models to improve predictive model performance
and robustness.
| Version: |
0.1.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
pROC, progress, rlang |
| Suggests: |
knitr, lightgbm, msaenet, rmarkdown, xgboost |
| Published: |
2024-04-30 |
| DOI: |
10.32614/CRAN.package.stackgbm |
| Author: |
Nan Xiao  [aut,
cre, cph] |
| Maintainer: |
Nan Xiao <me at nanx.me> |
| BugReports: |
https://github.com/nanxstats/stackgbm/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://nanx.me/stackgbm/, https://github.com/nanxstats/stackgbm |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
stackgbm results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=stackgbm
to link to this page.