Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Mastering Machine Learning with R, Second Edition

You're reading from   Mastering Machine Learning with R, Second Edition Advanced prediction, algorithms, and learning methods with R 3.x

Arrow left icon
Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787287471
Length 420 pages
Edition 2nd Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
Arrow right icon
View More author details
Toc

Table of Contents (17) Chapters Close

Preface 1. A Process for Success FREE CHAPTER 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
You have been reading a chapter from
Mastering Machine Learning with R, Second Edition - Second Edition
Published in: Apr 2017
Publisher: Packt
ISBN-13: 9781787287471
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime