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