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).


Reference manual

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1.0.0 by Jean Bertin, a month ago

Browse source code at

Authors: Jean Bertin

Documentation:   PDF Manual  

GPL-3 license

Imports shiny, argonDash, argonR, shinyjs, shinydashboard, h2o, shinyWidgets, dygraphs, plotly, sparklyr, tidyr, DT, ggplot2, shinycssloaders, lubridate, lifecycle, graphics

Depends on dplyr, data.table

Suggests knitr, rmarkdown, covr, testthat

See at CRAN