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FeaLect  

Scores Features for Feature Selection
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("FeaLect", "1.20")



Attach the package and use:
library("FeaLect")
Maintained by
Habil Zare
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2010-11-03
Latest Update: 2020-02-25
Description:
For each feature, a score is computed that can be useful for feature selection. Several random subsets are sampled from the input data and for each random subset, various linear models are fitted using lars method. A score is assigned to each feature based on the tendency of LASSO in including that feature in the models.Finally, the average score and the models are returned as the output. The features with relatively low scores are recommended to be ignored because they can lead to overfitting of the model to the training data. Moreover, for each random subset, the best set of features in terms of global error is returned. They are useful for applying Bolasso, the alternative feature selection method that recommends the intersection of features subsets.
How to cite:
Habil Zare (2010). FeaLect: Scores Features for Feature Selection. R package version 1.20, https://cran.r-project.org/web/packages/FeaLect. Accessed 23 Sep. 2026.
Previous versions and publish date:
1.1 (2010-11-03 08:32), 1.3 (2012-10-29 08:57), 1.7 (2014-12-31 05:47), 1.8 (2015-03-08 15:48), 1.10 (2015-05-13 00:55), 1.12 (2018-05-16 22:37), 1.14 (2018-06-01 15:13), (2026-07-09 08:04)
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Complete documentation for FeaLect
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