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).
Version: 2.0.4 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) Published: 2023-03-17 DOI: 10.32614/CRAN.package.spFSR Author: David Akman [aut, cre], Babak Abbasi [aut, ctb], Yong Kai Wong [aut, ctb], Guo Feng Anders Yeo [aut, ctb], Zeren D. Yenice [ctb] Maintainer: David Akman <david.v.akman at gmail.com> BugReports: https://github.com/yongkai17/spFSR/issues License: GPL-3 URL: https://www.featureranking.com/ NeedsCompilation: no CRAN checks: spFSR results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=spFSR to link to this page.
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