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swag  

Sparse Wrapper Algorithm
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("swag", "0.1.0")



Attach the package and use:
library("swag")
Maintained by
Samuel Orso
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-11-10
Latest Update: 2020-11-10
Description:
An algorithm that trains a meta-learning procedure that combines screening and wrapper methods to find a set of extremely low-dimensional attribute combinations. This package works on top of the 'caret' package and proceeds in a forward-step manner. More specifically, it builds and tests learners starting from very few attributes until it includes a maximal number of attributes by increasing the number of attributes at each step. Hence, for each fixed number of attributes, the algorithm tests various (randomly selected) learners and picks those with the best performance in terms of training error. Throughout, the algorithm uses the information coming from the best learners at the previous step to build and test learners in the following step. In the end, it outputs a set of strong low-dimensional learners.
How to cite:
Samuel Orso (2020). swag: Sparse Wrapper Algorithm. R package version 0.1.0, https://cran.r-project.org/web/packages/swag. Accessed 22 Dec. 2024.
Previous versions and publish date:
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Complete documentation for swag
Functions, R codes and Examples using the swag R package
Some associated functions: predict.swag . swag . swagControl . 
Some associated R codes: control.R . predict.R . swag.R .  Full swag package functions and examples
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