fastAdaboost
1.0.0a Fast Implementation of Adaboost
Overview
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.
Install
Health
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Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 38%
Downloads
Dependencies
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Code & Tests
People & History
1 release. R releases are shown for context.
- RR 4.3.0 released · 2023-04-21
- archivedRemoved from CRAN2022-09-18issues were not corrected in time
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- RR 3.3.0 released · 2016-05-03
- 1.0.02016-02-28
- RR 3.2.0 released · 2015-04-16
Package metadata
- Total releases
- 1
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.1.2
- Download size
- not tracked yet
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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Run in R for the authors' preferred citation:
citation("fastAdaboost")This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.
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