CRAN/E | spFSR

spFSR

Feature Selection and Ranking via Simultaneous Perturbation Stochastic Approximation

Installation

About

An implementation of feature selection, weighting and ranking via simultaneous perturbation stochastic approximation (SPSA). The SPSA-FSR algorithm searches for a locally optimal set of features that yield the best predictive performance using some error measures such as mean squared error (for regression problems) and accuracy rate (for classification problems).

www.featureranking.com/
Bug report File report

Key Metrics

Version 2.0.4
Published 2023-03-17 398 days ago
Needs compilation? no
License GPL-3
CRAN checks spFSR results

Downloads

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Maintainer

Maintainer

David Akman

david.v.akman@gmail.com

Authors

David Akman

aut / cre

Babak Abbasi

aut / ctb

Yong Kai Wong

aut / ctb

Guo Feng Anders Yeo

aut / ctb

Zeren D. Yenice

ctb

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

spFSR archive

Depends

mlr3 ≥ 0.14.0
future ≥ 1.28.0
tictoc ≥ 1.0

Imports

mlr3pipelines ≥ 0.4.2
mlr3learners ≥ 0.5.4
ranger ≥0.14.1
parallel ≥ 3.4.2
ggplot2 ≥ 2.2.1
lgr ≥0.4.4

Suggests

caret ≥ 6.0
MASS ≥ 7.3