Package: ExhaustiveSearch 1.0.1

Rudolf Jagdhuber

ExhaustiveSearch: A Fast and Scalable Exhaustive Feature Selection Framework

The goal of this package is to provide an easy to use, fast and scalable exhaustive search framework. Exhaustive feature selections typically require a very large number of models to be fitted and evaluated. Execution speed and memory management are crucial factors here. This package provides solutions for both. Execution speed is optimized by using a multi-threaded C++ backend, and memory issues are solved by by only storing the best results during execution and thus keeping memory usage constant.

Authors:Rudolf Jagdhuber [aut, cre], Jorge Nocedal [cph], Naoaki Okazaki [cph]

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ExhaustiveSearch.pdf |ExhaustiveSearch.html✨
ExhaustiveSearch/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/rudolfjagdhuber/exhaustivesearch/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

aicexhaustive-searchfeature-selectionlinear-regressionlogistic-regressionmachine-learningmodel-selectionmse

3.60 score 4 stars 5 scripts 138 downloads 2 mentions 3 exports 2 dependencies

Last updated 4 years agofrom:d43403ae76. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 06 2024
R-4.5-win-x86_64NOTENov 06 2024
R-4.5-linux-x86_64NOTENov 06 2024
R-4.4-win-x86_64NOTENov 06 2024
R-4.4-mac-x86_64NOTENov 06 2024
R-4.4-mac-aarch64NOTENov 06 2024
R-4.3-win-x86_64NOTENov 06 2024
R-4.3-mac-x86_64NOTENov 06 2024
R-4.3-mac-aarch64NOTENov 06 2024

Exports:ExhaustiveSearchgetFeaturesresultTable

Dependencies:RcppRcppArmadillo