R code to accompany Real-World Machine Learning (Chapter 2)

October 1, 2016
By

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

Abstract

Introduces my Github repo providing R code to accompany the book “Real-World Machine Learning”.

Introducing rwml-R

The book “Real-World Machine Learning” attempts to prepare the reader for the
realities of machine learning. It covers a basic framework for
machine-learning projects, then it dives into extended examples that show how
that basic framework can be applied in realistic situations. It attempts to
provide the “hidden wisdom” on how to go about implementing products and
solutions based on machine learning. The book is a relatively easy read and
definitely worth the investment in time, but all of the supplied code is
contained in
iPython notebooks. I’m working through the book, reproducing all of the code
listings and figures using R markdown, and I’m posting
the results in a github repo: rwml-R.
If you find this project helpful, find any errors, or have any suggestions,
please leave a comment below or use the Tweet button.

Example: Mosaic Plot in Figure 2.12

To reproduce the mosaic plot in Figure 2.12 of the book, I use the vcd
package which contains a plethora of excellent tools for exploring categorical
data. The below mosaic plot shows the relationship between passenger gender
and survival in the supplied Titanic Passengers dataset.

Plot generated by above code

Feedback welcome

I’d love to hear from you if you find this project helpful or if you
have any suggestions. Please leave a comment below or use the Tweet button.
Also, feel free to fork the rwml-R repo and
submit a pull request if you want to contribute.

Download
Fork

To leave a comment for the author, please follow the link and comment on their blog: data prone - R.

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