Book Image

Machine Learning with R - Third Edition

By : Brett Lantz
Book Image

Machine Learning with R - Third Edition

By: Brett Lantz

Overview of this book

Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.
Table of Contents (18 chapters)
Machine Learning with R - Third Edition
Contributors
Preface
Other Books You May Enjoy
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Index

Example – filtering mobile phone spam with the Naive Bayes algorithm


As the worldwide use of mobile phones has grown, a new avenue for electronic junk mail has opened for disreputable marketers. These advertisers utilize short message service (SMS) text messages to target potential consumers with unwanted advertising known as SMS spam. This type of spam is troublesome because, unlike email spam, an SMS message is particularly disruptive, due to the omnipresence of one's mobile phone. Developing a classification algorithm that could filter SMS spam would provide a useful tool for cellular phone providers.

Since Naive Bayes has been used successfully for email spam filtering, it seems likely that it could also be applied to SMS spam. However, relative to email spam, SMS spam poses additional challenges for automated filters. SMS messages are often limited to 160 characters, reducing the amount of text that can be used to identify whether a message is junk. The limit, combined with small mobile...