Getting started with the shinyML package

Jean Bertin



shinyML is a Shiny application that helps you to easily compare supervised machine learning regression models. The two main functions of this package are shinyML_regression and shinyML_classification which leaves the choice for the user to train and test models on H2O or Spark framework.

Introduction of shinyML

Once you get your data stored on a data.table or data.frame object, you can just use one line of code to run shinyML_regression or shinyML_classification functions in order to automatically deploy an interactive shiny app. This app can be shared your colleagues if you put share_app argument to TRUE and select a port that is free on your server.


# An example of regression task 
shinyML_regression(data = iris,y = "Petal.Width",framework = "h2o")

# An example of classification task  
shinyML_classification(data = iris,y = "Species",framework = "h2o")

Explore input dataset before running the models…

Before running machine learning models, it can be useful to inspect the distribution of each variable and to have an insight of dependencies between explanatory variables. BothshinyML_regression and shinyML_classification functions allows to check classes of explanatory variables, plot histograms of each distribution and show correlation matrix between all variables. This tabs can be used to determine if some variable are strongly correlated to another and eventually removed from the training phase.You can also plot variation of every variable as a function of another using the “Explore dataset” tab.

An example of output of shinyML

Running the app…

Once the shiny app has been launched, you can manually adjust main parameters of regression or classification supervised models (such as generalized linear regression, Logistic regression, Naive Bayes, Random forest, Neural Network, Gradient Boosting …) by moving the corresponding cursors. In addition to hyper-parameters setting for each model, you can adjust train and test period and choose which explanatory variables you want to keep during the training phase.

An example of output of shinyML

You can then run each model separately or run all models simultaneously clicking the corresponding button to each box.