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sboost  

Machine Learning with AdaBoost on Decision Stumps
View on CRAN: Click here


Download and install sboost package within the R console
Install from CRAN:
install.packages("sboost")

Install from Github:
library("remotes")
install_github("cran/sboost")

Install by package version:
library("remotes")
install_version("sboost", "0.1.2")



Attach the package and use:
library("sboost")
Maintained by
Jadon Wagstaff
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-10-28
Latest Update: 2022-05-26
Description:
Creates classifier for binary outcomes using Adaptive Boosting (AdaBoost) algorithm on decision stumps with a fast C++ implementation. For a description of AdaBoost, see Freund and Schapire (1997) . This type of classifier is nonlinear, but easy to interpret and visualize. Feature vectors may be a combination of continuous (numeric) and categorical (string, factor) elements. Methods for classifier assessment, predictions, and cross-validation also included.
How to cite:
Jadon Wagstaff (2018). sboost: Machine Learning with AdaBoost on Decision Stumps. R package version 0.1.2, https://cran.r-project.org/web/packages/sboost. Accessed 22 Dec. 2024.
Previous versions and publish date:
0.1.0 (2018-10-28 23:40), 0.1.1 (2019-04-08 20:40)
Other packages that cited sboost R package
View sboost citation profile
Other R packages that sboost depends, imports, suggests or enhances
Complete documentation for sboost
Functions, R codes and Examples using the sboost R package
Some associated functions: assess . malware . mushrooms . predict.sboost_classifier . sboost . validate . 
Some associated R codes: RcppExports.R . assess.R . data.R . mean.R . predict.R . process_input.R . process_output.R . regression.R . sboost.R . validate.R .  Full sboost package functions and examples
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