shinyMlr

This article is originally published at https://mlr-org.github.io

shinyMlr is a web application, built with the R-package “shiny” that provides a user interface for mlr. By wrapping the main functionalities of mlr into our app, as well as implementing additional features for data visualisation and data preprocessing, we built a widely usable application for your day to day machine learning tasks, which we would like to present to you today.

Stefan and me started working on this project late summer 2016 as part of a practical course we attended for our Master’s program. We enjoyed the work on this project and will continue to maintain and extend our app in the future. However, after almost one year of work our application got a versatile tool and it is time to present it to a broader audience. To introduce you to the workflow and main features of our app, we uploaded a video series to our youtube channel. The videos are little tutorials that illustrate the workflow in form of a use case: We used the titanic data set from the kaggle competition as example data to show you step by step how it can be analyzed with our application.

The first video gives a small introduction and shows you how data can be imported:

In the next tutorial you will learn how to visualise your data and preprocess it:

The third and fourth screencasts show you how to create your task and how to construct and modify our built-in learning algorithms:

The fifth part of our tutorials shows you how to tune your learners to find suitable parameter settings for your given training set:

The sixth video gives you detailed information on how to actually train models on your task, predict on new data and plot model diagnostic and prediction plots:

The seventh video runs a benchmark experiment, to show you how to compare different learners in our application:

The last tutorial briefly demonstrates how to render an interactive report from your analysis done with our app:

I hope you enjoyed watching the videos and learned how to make use of our application. If you like working with our app please leave us a star and follow us on github

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This article is originally published at https://mlr-org.github.io
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