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A basic, clear implementation of tree-based gradient boosting designed to illustrate the core operation of boosting models. Tuning parameters (such as stochastic subsampling, modified learning rate, or regularization) are not implemented. The only adjustable parameter is the number of training rounds. If you are looking for a high performance boosting implementation with tuning parameters, consider the 'xgboost' package.
| Version: | 0.1.1 |
| Depends: | R (≥ 3.1.1), rpart (≥ 4.1-10) |
| Suggests: | testthat |
| Published: | 2016-04-19 |
| DOI: | 10.32614/CRAN.package.DidacticBoost |
| Author: | David Shaub [aut, cre] |
| Maintainer: | David Shaub <davidshaub at gmx.com> |
| BugReports: | https://github.com/dashaub/DidacticBoost/issues |
| License: | GPL-3 |
| URL: | https://github.com/dashaub/DidacticBoost |
| NeedsCompilation: | no |
| CRAN checks: | DidacticBoost results |
| Reference manual: | DidacticBoost.pdf |
| Package source: | DidacticBoost_0.1.1.tar.gz |
| Windows binaries: | r-devel: DidacticBoost_0.1.1.zip, r-release: DidacticBoost_0.1.1.zip, r-oldrel: DidacticBoost_0.1.1.zip |
| macOS binaries: | r-release (arm64): DidacticBoost_0.1.1.tgz, r-oldrel (arm64): DidacticBoost_0.1.1.tgz, r-release (x86_64): DidacticBoost_0.1.1.tgz, r-oldrel (x86_64): DidacticBoost_0.1.1.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.