mlr3shiny: Machine Learning in Shiny with mlr3

Build Status

This application provides the basic steps of a machine learning workflow from a graphical user interface built with Shiny. It uses the functionalities of the R-package mlr3.

Current functionalities of mlr3shiny are: Data import Creation of a task for supervised learning (regression, classification) Use of a set of algorithms as learners Training and evaluation of the generated models Benchmarking to compare several learners on a task simultaneously Prediction on new data using the trained learner


Install the package in R from GitHub.



Launch the application via:


Usage Description

Navigate over the different steps of the workflow using the menu bar. The tabs are chronologically ordered. The question mark in the top-right corner provides more information on the functionalities and purpose of each section. Start by importing a dataset. Then define a task (the problem to be solved) in the 'task' tab. Example tasks are already provided. Select different learners (algorithms) in the 'learner' tab and train and evaluate a model in 'train & evaluate'. Resampling strategies can be applied in a sub-section of 'train & evaluate'. Alternatively, different learners can be compared in a benchmark. Use the final model to make a prediction on new data in the 'predict' tab.

References to Algorithms

Try the mlr3shiny package in your browser

Any scripts or data that you put into this service are public.

mlr3shiny documentation built on Sept. 30, 2021, 5:09 p.m.