MyNixOS website logo
Description

Machine Learning in 'shiny' with 'mlr3'.

A web-based graphical user interface to provide the basic steps of a machine learning workflow. It uses the functionalities of the 'mlr3' framework.

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
  • Explain trained learner

Reference

Tetzlaff, L. and Szepannek G. (2022): mlr3shiny—State-of-the-art machine learning made easy, SoftwareX 20, DOI: 10.1016/j.softx.2022.101246.

Installation

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")

Example

Launch the application via:

mlr3shiny::launchMlr3Shiny()

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. An explanation of the final model from the predict tab can be made in the 'explain' tab.

References to Algorithms

Metadata

Version

0.3.0

License

Unknown

Platforms (75)

    Darwin
    FreeBSD
    Genode
    GHCJS
    Linux
    MMIXware
    NetBSD
    none
    OpenBSD
    Redox
    Solaris
    WASI
    Windows
Show all
  • aarch64-darwin
  • aarch64-genode
  • aarch64-linux
  • aarch64-netbsd
  • aarch64-none
  • aarch64_be-none
  • arm-none
  • armv5tel-linux
  • armv6l-linux
  • armv6l-netbsd
  • armv6l-none
  • armv7a-darwin
  • armv7a-linux
  • armv7a-netbsd
  • armv7l-linux
  • armv7l-netbsd
  • avr-none
  • i686-cygwin
  • i686-darwin
  • i686-freebsd
  • i686-genode
  • i686-linux
  • i686-netbsd
  • i686-none
  • i686-openbsd
  • i686-windows
  • javascript-ghcjs
  • loongarch64-linux
  • m68k-linux
  • m68k-netbsd
  • m68k-none
  • microblaze-linux
  • microblaze-none
  • microblazeel-linux
  • microblazeel-none
  • mips-linux
  • mips-none
  • mips64-linux
  • mips64-none
  • mips64el-linux
  • mipsel-linux
  • mipsel-netbsd
  • mmix-mmixware
  • msp430-none
  • or1k-none
  • powerpc-netbsd
  • powerpc-none
  • powerpc64-linux
  • powerpc64le-linux
  • powerpcle-none
  • riscv32-linux
  • riscv32-netbsd
  • riscv32-none
  • riscv64-linux
  • riscv64-netbsd
  • riscv64-none
  • rx-none
  • s390-linux
  • s390-none
  • s390x-linux
  • s390x-none
  • vc4-none
  • wasm32-wasi
  • wasm64-wasi
  • x86_64-cygwin
  • x86_64-darwin
  • x86_64-freebsd
  • x86_64-genode
  • x86_64-linux
  • x86_64-netbsd
  • x86_64-none
  • x86_64-openbsd
  • x86_64-redox
  • x86_64-solaris
  • x86_64-windows