FeaLect: Scores Features for Feature Selection

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.

AuthorHabil Zare
Date of publication2015-05-13 00:55:14
MaintainerHabil Zare <zare@txstate.edu>
LicenseGPL (>= 2)
Version1.10

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Files

FeaLect
FeaLect/inst
FeaLect/inst/doc
FeaLect/inst/doc/FeaLect_feature_scorer.Rnw
FeaLect/inst/doc/FeaLect_feature_scorer.R
FeaLect/inst/doc/FeaLect_feature_scorer.pdf
FeaLect/NAMESPACE
FeaLect/data
FeaLect/data/mcl_sll.rda
FeaLect/R
FeaLect/R/doctor.validate.R FeaLect/R/FeaLect-internal.R FeaLect/R/FeaLect.R FeaLect/R/train.doctor.R FeaLect/R/random.subset.R FeaLect/R/ignore.redundant.R FeaLect/R/compute.logistic.score.R FeaLect/R/compute.balanced.R FeaLect/R/input.check.FeaLect.R
FeaLect/vignettes
FeaLect/vignettes/FeaLect_feature_scorer.Rnw
FeaLect/vignettes/overfitting.bib
FeaLect/MD5
FeaLect/build
FeaLect/build/vignette.rds
FeaLect/DESCRIPTION
FeaLect/man
FeaLect/man/input.check.FeaLect.Rd FeaLect/man/compute.logistic.score.Rd FeaLect/man/random.subset.Rd FeaLect/man/train.doctor.Rd FeaLect/man/doctor.validate.Rd FeaLect/man/compute.balanced.Rd FeaLect/man/FeaLect-package.Rd FeaLect/man/ignore.redundant.Rd FeaLect/man/mcl_sll.Rd FeaLect/man/FeaLect.Rd

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