This tool enables in-database scoring of 'XGBoost' models built in R, by translating trained model objects into SQL query. 'XGBoost' <https://xgboost.readthedocs.io/en/latest/index.html> provides parallel tree boosting (also known as gradient boosting machine, or GBM) algorithms in a highly efficient, flexible and portable way. GBM algorithm is introduced by Friedman (2001) <doi:10.1214/aos/1013203451>, and more details on 'XGBoost' can be found in Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.
| Version: | 0.1.2 |
| Depends: | R (≥ 3.1.0) |
| Imports: | xgboost (≥ 0.81.0.1), data.table (≥ 1.12.0) |
| Suggests: | ggplot2, knitr, rmarkdown |
| Published: | 2019-03-13 |
| Author: | Chengjun Hou [aut, cre], Abhishek Bishoyi [aut] |
| Maintainer: | Chengjun Hou <chengjun.hou at gmail.com> |
| BugReports: | https://github.com/chengjunhou/xgb2sql/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/chengjunhou/xgb2sql |
| NeedsCompilation: | no |
| CRAN checks: | xgb2sql results |
| Reference manual: | xgb2sql.pdf |
| Vignettes: |
Deploy XGBoost Model as SQL Query |
| Package source: | xgb2sql_0.1.2.tar.gz |
| Windows binaries: | r-devel: xgb2sql_0.1.2.zip, r-release: xgb2sql_0.1.2.zip, r-oldrel: xgb2sql_0.1.2.zip |
| macOS binaries: | r-release: xgb2sql_0.1.2.tgz, r-oldrel: xgb2sql_0.1.2.tgz |
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