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Machine Learning with R

You're reading from   Machine Learning with R Expert techniques for predictive modeling to solve all your data analysis problems

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Product type Paperback
Published in Jul 2015
Publisher Packt
ISBN-13 9781784393908
Length 452 pages
Edition 2nd Edition
Languages
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Author (1):
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Brett Lantz Brett Lantz
Author Profile Icon Brett Lantz
Brett Lantz
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Table of Contents (14) Chapters Close

Preface 1. Introducing Machine Learning FREE CHAPTER 2. Managing and Understanding Data 3. Lazy Learning – Classification Using Nearest Neighbors 4. Probabilistic Learning – Classification Using Naive Bayes 5. Divide and Conquer – Classification Using Decision Trees and Rules 6. Forecasting Numeric Data – Regression Methods 7. Black Box Methods – Neural Networks and Support Vector Machines 8. Finding Patterns – Market Basket Analysis Using Association Rules 9. Finding Groups of Data – Clustering with k-means 10. Evaluating Model Performance 11. Improving Model Performance 12. Specialized Machine Learning Topics Index

Chapter 12. Specialized Machine Learning Topics

Congratulations on reaching this point in your machine learning journey! If you have not already started work on your own projects, you will do so soon. And in doing so, you may find that the task of turning data into action is more difficult than it first appeared.

As you gathered data, you may have realized that the information was trapped in a proprietary format or spread across pages on the Web. Making matters worse, after spending hours reformatting the data, maybe your computer slowed to a crawl after running out of memory. Perhaps R even crashed or froze your machine. Hopefully, you were undeterred, as these issues can be remedied with a bit more effort.

This chapter covers techniques that may not apply to every project, but will prove useful for working around such specialized issues. You might find the information particularly useful if you tend to work with data that is:

  • Stored in unstructured or proprietary formats such as...
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