souravc83. See also theR-universe documentation.Package: fastAdaboost 1.0.0
Sourav Chatterjee
fastAdaboost: a Fast Implementation of Adaboost
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.
Authors:
fastAdaboost_1.0.0.tar.gz
fastAdaboost_1.0.0.zip(r-4.5)fastAdaboost_1.0.0.zip(r-4.4)fastAdaboost_1.0.0.zip(r-4.3)
fastAdaboost_1.0.0.tgz(r-4.4-x86_64)fastAdaboost_1.0.0.tgz(r-4.4-arm64)fastAdaboost_1.0.0.tgz(r-4.3-x86_64)fastAdaboost_1.0.0.tgz(r-4.3-arm64)
fastAdaboost_1.0.0.tar.gz(r-4.5-noble)fastAdaboost_1.0.0.tar.gz(r-4.4-noble)
fastAdaboost_1.0.0.tgz(r-4.4-emscripten)fastAdaboost_1.0.0.tgz(r-4.3-emscripten)
fastAdaboost.pdf |fastAdaboost.html✨
fastAdaboost/json (API)
| # Install 'fastAdaboost' in R: |
| install.packages('fastAdaboost', repos = c('https://rickhelmus.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/souravc83/fastadaboost/issues
Last updated 9 years agofrom:f331ff8ccf. Checks:9 OK. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 05 2025 |
| R-4.5-win-x86_64 | OK | Jan 05 2025 |
| R-4.5-linux-x86_64 | OK | Jan 05 2025 |
| R-4.4-win-x86_64 | OK | Jan 05 2025 |
| R-4.4-mac-x86_64 | OK | Jan 05 2025 |
| R-4.4-mac-aarch64 | OK | Jan 05 2025 |
| R-4.3-win-x86_64 | OK | Jan 05 2025 |
| R-4.3-mac-x86_64 | OK | Jan 05 2025 |
| R-4.3-mac-aarch64 | OK | Jan 05 2025 |
Exports:adaboostget_treereal_adaboost
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Adaboost.M1 algorithm | adaboost |
| fastAdaboost: fast adaboost implementation for R | fastAdaboost-package fastAdaboost |
| Fetches a decision tree | get_tree |
| predict method for adaboost objects | predict.adaboost |
| predict method for real_adaboost objects | predict.real_adaboost |
| Print adaboost.m1 model summary | print.adaboost |
| Print real adaboost model summary | print.real_adaboost |
| Real Adaboost algorithm | real_adaboost |
