Package: shinyML 1.0.1

Jean Bertin

shinyML: Compare Supervised Machine Learning Models Using Shiny App

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).

Authors:Jean Bertin

shinyML_1.0.1.tar.gz
shinyML_1.0.1.zip(r-4.5)shinyML_1.0.1.zip(r-4.4)shinyML_1.0.1.zip(r-4.3)
shinyML_1.0.1.tgz(r-4.4-any)shinyML_1.0.1.tgz(r-4.3-any)
shinyML_1.0.1.tar.gz(r-4.5-noble)shinyML_1.0.1.tar.gz(r-4.4-noble)
shinyML_1.0.1.tgz(r-4.4-emscripten)shinyML_1.0.1.tgz(r-4.3-emscripten)
shinyML.pdf |shinyML.html✨
shinyML/json (API)
NEWS

Peer review:

Bug tracker:https://github.com/jeanbertinr/shinyml/issues

On CRAN:

2 exports 56 stars 3.25 score 102 dependencies 13 scripts 298 downloads

Last updated 4 years agofrom:2aecbf230b. Checks:OK: 7. Indexed: yes.

Exports:shinyML_classificationshinyML_regression

Dependencies:anytimeargonDashargonRaskpassbase64encBHbitopsblobbslibcachemclicodetoolscolorspacecommonmarkconfigcpp11crayoncrosstalkcurldata.tableDBIdbplyrdigestdplyrDTdygraphsevaluatefansifarverfastmapfontawesomefsgenericsggplot2globalsgluegtableh2ohighrhtmltoolshtmlwidgetshttpuvhttrisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelubridatemagrittrMASSMatrixmemoisemgcvmimemunsellnlmeopensslpillarpkgconfigplotlypromisespurrrR6rappdirsRColorBrewerRcppRCurlrlangrmarkdownrstudioapisassscalesshinyshinycssloadersshinyjsshinyWidgetssourcetoolssparklyrstringistringrsystibbletidyrtidyselecttimechangetinytexutf8uuidvctrsviridisLitewithrxfunxml2xtablextsyamlzoo