Package: spFSR 1.0.0
Vural Aksakalli
spFSR: Feature Selection and Ranking by Simultaneous Perturbation Stochastic Approximation
An implementation of feature selection and ranking via simultaneous perturbation stochastic approximation (SPSA-FSR) based on works by V. Aksakalli and M. Malekipirbazari (2015) <arxiv:1508.07630> and Zeren D. Yenice and et al. (2018) <arxiv:1804.05589>. The SPSA-FSR algorithm searches for a locally optimal set of features that yield the best predictive performance using a specified error measure such as mean squared error (for regression problems) and accuracy rate (for classification problems). This package requires an object of class 'task' and an object of class 'Learner' from the 'mlr' package.
Authors:
spFSR_1.0.0.tar.gz
spFSR_1.0.0.zip(r-4.5)spFSR_1.0.0.zip(r-4.4)spFSR_1.0.0.zip(r-4.3)
spFSR_1.0.0.tgz(r-4.4-any)spFSR_1.0.0.tgz(r-4.3-any)
spFSR_1.0.0.tar.gz(r-4.5-noble)spFSR_1.0.0.tar.gz(r-4.4-noble)
spFSR_1.0.0.tgz(r-4.4-emscripten)spFSR_1.0.0.tgz(r-4.3-emscripten)
spFSR.pdf |spFSR.html✨
spFSR/json (API)
| # Install 'spFSR' in R: |
| install.packages('spFSR', repos = c('https://yongkai17.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/yongkai17/spfsr/issues
Last updated 6 years agofrom:0ca92c1d71. Checks:OK: 1 WARNING: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 10 2024 |
| R-4.5-win | WARNING | Nov 10 2024 |
| R-4.5-linux | WARNING | Nov 10 2024 |
| R-4.4-win | WARNING | Nov 10 2024 |
| R-4.4-mac | WARNING | Nov 10 2024 |
| R-4.3-win | WARNING | Nov 10 2024 |
| R-4.3-mac | WARNING | Nov 10 2024 |
Exports:getBestModelgetImportanceplotImportancespFeatureSelection
Dependencies:backportsBBmisccheckmateclassclicolorspacedata.tablefansifarverfastmatchggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmlbenchmlrmunsellnlmeparallelMapParamHelperspillarpkgconfigR6RColorBrewerrlangscalesstringisurvivaltibbletictocutf8vctrsviridisLitewithrXML
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Extract the wrapped model of the best performing features from an spFSR object | getBestModel |
| Extract feature importance data from a spFSR object | getImportance |
| Plot an spFSR object | plot.spFSR |
| Plot importance ranks of best performing features from a spFSR object | plotImportance |
| Feature selection and ranking by SPSA-FSR | spFeatureSelection |
| Default function of feature selection and ranking by SP-FSR | spFSR.default |
| Summarising an spFSR object | summary.spFSR |