Fast and automatic gradient tree boosting designed to avoid manual tuning and cross-validation by utilizing an information theoretic approach. This makes the algorithm adaptive to the dataset at hand; it is completely automatic, and with minimal worries of overfitting. Consequently, the speed-ups relative to state-of-the-art implementations can be in the thousands while mathematical and technical knowledge required on the user are minimized.
| Version: | 0.9.1 |
| Depends: | R (≥ 3.6.0) |
| Imports: | methods, Rcpp (≥ 1.0.1) |
| LinkingTo: | Rcpp, RcppEigen |
| Suggests: | testthat |
| Published: | 2020-10-13 |
| Author: | Berent Ånund Strømnes Lunde |
| Maintainer: | Berent Ånund Strømnes Lunde <lundeberent at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | agtboost results |
| Reference manual: | agtboost.pdf |
| Package source: | agtboost_0.9.1.tar.gz |
| Windows binaries: | r-devel: agtboost_0.9.1.zip, r-release: agtboost_0.9.1.zip, r-oldrel: agtboost_0.9.1.zip |
| macOS binaries: | r-release: agtboost_0.9.1.tgz, r-oldrel: agtboost_0.9.1.tgz |
| Old sources: | agtboost archive |
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