shinyMlr

May 15, 2017
By

(This article was first published on mlr-org, and kindly contributed to R-bloggers)

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

To leave a comment for the author, please follow the link and comment on their blog: mlr-org.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)