The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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 * Explain trained learner
Tetzlaff, L. and Szepannek G. (2022): mlr3shiny—State-of-the-art machine learning made easy, SoftwareX 20, DOI: 10.1016/j.softx.2022.101246.
Install the package in R via CRAN:
install.packages(mlr3shiny)Install the development version of the package in R from GitHub.
remotes::install_github("https://github.com/LamaTe/mlr3shiny.git")Launch the application via:
mlr3shiny::launchMlr3Shiny()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. An explanation of the final model from the predict tab can be made in the ‘explain’ tab.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.