Implementation of a shiny app to easily compare supervised machine learning model performances. You provide the data and configure each model parameter directly on the shiny app. Different supervised learning algorithms can be tested either on Spark or H2O frameworks to suit your regression and classification tasks. Implementation of available machine learning models on R has been done by Lantz (2013, ISBN:9781782162148).
Version: 1.0.1 Depends: dplyr, data.table Imports: shiny (≥ 1.0.3), argonDash, argonR, shinyjs, h2o, shinyWidgets, dygraphs, plotly, sparklyr, tidyr, DT, ggplot2, shinycssloaders, lubridate, graphics Suggests: knitr, rmarkdown, covr, testthat Published: 2021-02-24 DOI: 10.32614/CRAN.package.shinyML Author: Jean Bertin Maintainer: Jean Bertin <jean.bertin at mines-paris.org> BugReports: https://github.com/JeanBertinR/shinyML/issues License: GPL-3 URL: https://jeanbertinr.github.io/shinyMLpackage/ NeedsCompilation: no Materials: README NEWS CRAN checks: shinyML results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=shinyML to link to this page.
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