FeaLect: Scores Features for Feature Selection
Version 1.10

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
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("FeaLect")

Getting started

Package overview

Popular man pages

doctor.validate: Validates a model using validaing samples.
FeaLect: Computes the scores of the features.
FeaLect-package: Scores features for Feature seLection
ignore.redundant: Refines a feature matrix
mcl_sll: MCL and SLL lymphoma subtypes
random.subset: Selects a random subset of the input.
train.doctor: Fittes various models based on a combination on penalized...
See all...

All man pages Function index File listing

Man pages

compute.balanced: Balances between negative and positive samples by...
compute.logistic.score: Fits a logistic regression model using the linear scores
doctor.validate: Validates a model using validaing samples.
FeaLect: Computes the scores of the features.
FeaLect-package: Scores features for Feature seLection
ignore.redundant: Refines a feature matrix
input.check.FeaLect: Checks the inputs to Fealect() function.
mcl_sll: MCL and SLL lymphoma subtypes
random.subset: Selects a random subset of the input.
train.doctor: Fittes various models based on a combination on penalized...

Functions

FeaLect Man page Man page Source code
FeaLect-package Man page
compute.balanced Man page Source code
compute.logistic.score Man page Source code
doctor.validate Man page Source code
ignore.redundant Man page Source code
input.check.FeaLect Man page Source code
mcl_sll Man page
random.subset Man page Source code
train.doctor Man page Source code

Files

inst
inst/doc
inst/doc/FeaLect_feature_scorer.Rnw
inst/doc/FeaLect_feature_scorer.R
inst/doc/FeaLect_feature_scorer.pdf
NAMESPACE
data
data/mcl_sll.rda
R
R/doctor.validate.R
R/FeaLect-internal.R
R/FeaLect.R
R/train.doctor.R
R/random.subset.R
R/ignore.redundant.R
R/compute.logistic.score.R
R/compute.balanced.R
R/input.check.FeaLect.R
vignettes
vignettes/FeaLect_feature_scorer.Rnw
vignettes/overfitting.bib
MD5
build
build/vignette.rds
DESCRIPTION
man
man/input.check.FeaLect.Rd
man/compute.logistic.score.Rd
man/random.subset.Rd
man/train.doctor.Rd
man/doctor.validate.Rd
man/compute.balanced.Rd
man/FeaLect-package.Rd
man/ignore.redundant.Rd
man/mcl_sll.Rd
man/FeaLect.Rd
FeaLect documentation built on May 20, 2017, 4:57 a.m.

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