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: | 1.0.0 |
| Depends: | dplyr, data.table |
| Imports: | shiny (≥ 1.0.3), argonDash, argonR, shinyjs, shinydashboard, h2o, shinyWidgets, dygraphs, plotly, sparklyr, tidyr, DT, ggplot2, shinycssloaders, lubridate, lifecycle, graphics |
| Suggests: | knitr, rmarkdown, covr, testthat |
| Published: | 2020-10-03 |
| Author: | Jean Bertin |
| Maintainer: | Jean Bertin <jean.bertin at mines-paris.org> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | shinyML results |
| Reference manual: | shinyML.pdf |
| Vignettes: |
Getting started with shinyML |
| Package source: | shinyML_1.0.0.tar.gz |
| Windows binaries: | r-devel: shinyML_1.0.0.zip, r-release: shinyML_1.0.0.zip, r-oldrel: shinyML_1.0.0.zip |
| macOS binaries: | r-release: shinyML_1.0.0.tgz, r-oldrel: shinyML_1.0.0.tgz |
| Old sources: | shinyML archive |
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