https://github.com/nanxstats/stackgbm
🌳 Stacked Gradient Boosting Machines
https://github.com/nanxstats/stackgbm
automl catboost decision-trees ensemble-learning gbdt gbm gradient-boosting lightgbm machine-learning model-stacking xgboost
Last synced: 29 days ago
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🌳 Stacked Gradient Boosting Machines
- Host: GitHub
- URL: https://github.com/nanxstats/stackgbm
- Owner: nanxstats
- License: other
- Created: 2020-06-16T22:19:25.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2024-05-06T04:42:19.000Z (about 1 year ago)
- Last Synced: 2025-04-18T20:31:55.449Z (about 1 month ago)
- Topics: automl, catboost, decision-trees, ensemble-learning, gbdt, gbm, gradient-boosting, lightgbm, machine-learning, model-stacking, xgboost
- Language: R
- Homepage: https://nanx.me/stackgbm/
- Size: 1.91 MB
- Stars: 25
- Watchers: 2
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# stackgbm 
[](https://github.com/nanxstats/stackgbm/actions/workflows/R-CMD-check.yaml)
[](https://cran.r-project.org/package=stackgbm)
[](https://cran.r-project.org/package=stackgbm)
stackgbm offers a minimalist, research-oriented implementation of model stacking
([Wolpert, 1992](https://doi.org/10.1016/S0893-6080(05)80023-1))
for gradient boosted tree models built by
xgboost ([Chen and Guestrin, 2016](https://doi.org/10.1145/2939672.2939785)),
lightgbm ([Ke et al., 2017](https://dl.acm.org/doi/10.5555/3294996.3295074)),
and catboost ([Prokhorenkova et al., 2018](https://dl.acm.org/doi/abs/10.5555/3327757.3327770)).
## Installation
The easiest way to get stackgbm is to install from CRAN:
```r
install.packages("stackgbm")
```
Alternatively, to use a new feature or get a bug fix,
you can install the development version of stackgbm from GitHub:
```r
# install.packages("remotes")
remotes::install_github("nanxstats/stackgbm")
```
To install all potential dependencies, check out the instructions from
[manage dependencies](https://github.com/nanxstats/stackgbm/wiki/Manage-dependencies).
## Model
stackgbm implements a classic two-layer stacking model: the first layer
generates "features" produced by gradient boosting trees.
The second layer is a logistic regression that uses these features as inputs.
## Related projects
For a more comprehensive and flexible implementation of model stacking, see
[stacks](https://stacks.tidymodels.org) in tidymodels,
[mlr3pipelines](https://mlr-org.com/gallery/pipelines/2020-04-27-tuning-stacking/) in mlr3,
and [StackingClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingClassifier.html)
in scikit-learn.
## Code of Conduct
Please note that the stackgbm project is released with a
[Contributor Code of Conduct](https://nanx.me/stackgbm/CODE_OF_CONDUCT.html).
By contributing to this project, you agree to abide by its terms.