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Mastering Machine Learning with R, Second Edition - Second Edition

You're reading from  Mastering Machine Learning with R, Second Edition - Second Edition

Product type Book
Published in Apr 2017
Publisher Packt
ISBN-13 9781787287471
Pages 420 pages
Edition 2nd Edition
Languages
Toc

Table of Contents (23) Chapters close

Title Page
Credits
About the Author
About the Reviewers
Packt Upsell
Customer Feedback
Preface
1. A Process for Success 2. Linear Regression - The Blocking and Tackling of Machine Learning 3. Logistic Regression and Discriminant Analysis 4. Advanced Feature Selection in Linear Models 5. More Classification Techniques - K-Nearest Neighbors and Support Vector Machines 6. Classification and Regression Trees 7. Neural Networks and Deep Learning 8. Cluster Analysis 9. Principal Components Analysis 10. Market Basket Analysis, Recommendation Engines, and Sequential Analysis 11. Creating Ensembles and Multiclass Classification 12. Time Series and Causality 13. Text Mining 14. R on the Cloud 15. R Fundamentals 16. Sources

Algorithm flowchart


The purpose of this section is to create a tool that will help you not just select possible modeling techniques but also think deeper about the problem. The residual benefit is that it may help you frame the problem with the project sponsor/team. The techniques in the flowchart are certainly not comprehensive but are exhaustive enough to get you started. It also includes techniques not discussed in this book.

The following figure starts the flow of selecting the potential modeling techniques. As you answer the question(s), it will take you to one of the four additional charts:

Figure 2

If the data is text or in the time series format, then you will follow the flow in the following figure:

Figure 3

In this branch of the algorithm, you do not have text or time series data. You also do not want to predict a category, so you are looking to make recommendations, understand associations, or predict a quantity:

Figure 4

To get to this section, you will have data that is not text or time series. You want to categorize the data, but it does not have an outcome label, which brings us to clustering methods, as follows:

Figure 5

This brings us to the situation where we want to categorize the data and it is labeled, that is, classification:

Figure 6

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Mastering Machine Learning with R, Second Edition - Second Edition
Published in: Apr 2017 Publisher: Packt ISBN-13: 9781787287471
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