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stackgbm \u003cimg src=\"man/figures/logo.png\" align=\"right\" width=\"120\" /\u003e\n\n\u003c!-- badges: start --\u003e\n[![R-CMD-check](https://github.com/nanxstats/stackgbm/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/nanxstats/stackgbm/actions/workflows/R-CMD-check.yaml)\n[![CRAN status](https://www.r-pkg.org/badges/version/stackgbm)](https://cran.r-project.org/package=stackgbm)\n[![CRAN downloads](https://cranlogs.r-pkg.org/badges/stackgbm)](https://cran.r-project.org/package=stackgbm)\n\u003c!-- badges: end --\u003e\n\nstackgbm offers a minimalist, research-oriented implementation of model stacking\n([Wolpert, 1992](https://doi.org/10.1016/S0893-6080(05)80023-1))\nfor gradient boosted tree models built by\nxgboost ([Chen and Guestrin, 2016](https://doi.org/10.1145/2939672.2939785)),\nlightgbm ([Ke et al., 2017](https://dl.acm.org/doi/10.5555/3294996.3295074)),\nand catboost ([Prokhorenkova et al., 2018](https://dl.acm.org/doi/abs/10.5555/3327757.3327770)).\n\n## Installation\n\nThe easiest way to get stackgbm is to install from CRAN:\n\n```r\ninstall.packages(\"stackgbm\")\n```\n\nAlternatively, to use a new feature or get a bug fix,\nyou can install the development version of stackgbm from GitHub:\n\n```r\n# install.packages(\"remotes\")\nremotes::install_github(\"nanxstats/stackgbm\")\n```\n\nTo install all potential dependencies, check out the instructions from\n[manage dependencies](https://github.com/nanxstats/stackgbm/wiki/Manage-dependencies).\n\n## Model\n\nstackgbm implements a classic two-layer stacking model: the first layer\ngenerates \"features\" produced by gradient boosting trees.\nThe second layer is a logistic regression that uses these features as inputs.\n\n## Related projects\n\nFor a more comprehensive and flexible implementation of model stacking, see\n[stacks](https://stacks.tidymodels.org) in tidymodels,\n[mlr3pipelines](https://mlr-org.com/gallery/pipelines/2020-04-27-tuning-stacking/) in mlr3,\nand [StackingClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingClassifier.html)\nin scikit-learn.\n\n## Code of Conduct\n\nPlease note that the stackgbm project is released with a\n[Contributor Code of Conduct](https://nanx.me/stackgbm/CODE_OF_CONDUCT.html).\nBy contributing to this project, you agree to abide by its terms.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnanxstats%2Fstackgbm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnanxstats%2Fstackgbm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnanxstats%2Fstackgbm/lists"}