Package: FeaLect 1.20

Habil Zare

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.

Authors:Habil Zare

FeaLect_1.20.tar.gz
FeaLect_1.20.tar.gz(r-4.5-noble)FeaLect_1.20.tar.gz(r-4.4-noble)
FeaLect_1.20.tgz(r-4.4-emscripten)
FeaLect.pdf |FeaLect.html
FeaLect/json (API)

# Install 'FeaLect' in R:
install.packages('FeaLect', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • mcl_sll - MCL and SLL lymphoma subtypes

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.44 score 2 stars 23 scripts 209 downloads 6 mentions 8 exports 78 dependencies

Last updated 5 years agofrom:eb2479f638. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-linuxOKNov 20 2024

Exports:compute.balancedcompute.logistic.scoredoctor.validateFeaLectignore.redundantinput.check.FeaLectrandom.subsettrain.doctor

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercodetoolscolorspacedata.tabledigestevaluatefansifarverfastmapfontawesomeforeignFormulafsggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglarslatticelifecyclemagrittrMASSMatrixMatrixModelsmemoisemgcvmimemultcompmunsellmvtnormnlmennetpillarpkgconfigpolsplinequantregR6rappdirsRColorBrewerrlangrmarkdownrmsrpartrstudioapisandwichsassscalesSparseMstringistringrsurvivalTH.datatibbletinytexutf8vctrsviridisviridisLitewithrxfunyamlzoo

Feature seLection by computing statistical scores

Rendered fromFeaLect_feature_scorer.Rnwusingutils::Sweaveon Nov 20 2024.

Last update: 2018-05-16
Started: 2014-12-03