Implements Adaboost based on C++ backend code. This is blazingly fast and especially useful for large, in memory data sets. The package uses decision trees as weak classifiers. Once the classifiers have been trained, they can be used to predict new data. Currently, we support only binary classification tasks. The package implements the Adaboost.M1 algorithm and the real Adaboost(SAMME.R) algorithm.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.1.2) |
| Imports: | Rcpp, rpart |
| LinkingTo: | Rcpp (≥ 0.12.0) |
| Suggests: | testthat, knitr, MASS |
| Published: | 2016-02-28 |
| Author: | Sourav Chatterjee [aut, cre] |
| Maintainer: | Sourav Chatterjee <souravc83 at gmail.com> |
| BugReports: | https://github.com/souravc83/fastAdaboost/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/souravc83/fastAdaboost |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | fastAdaboost results |
| Reference manual: | fastAdaboost.pdf |
| Package source: | fastAdaboost_1.0.0.tar.gz |
| Windows binaries: | r-devel: fastAdaboost_1.0.0.zip, r-release: fastAdaboost_1.0.0.zip, r-oldrel: fastAdaboost_1.0.0.zip |
| macOS binaries: | r-release (arm64): fastAdaboost_1.0.0.tgz, r-oldrel (arm64): fastAdaboost_1.0.0.tgz, r-release (x86_64): fastAdaboost_1.0.0.tgz, r-oldrel (x86_64): fastAdaboost_1.0.0.tgz |
| Reverse depends: | fasi |
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