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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

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787287471
Length 420 pages
Edition 2nd Edition
Languages
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Author (1):
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Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
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Toc

Table of Contents (17) Chapters Close

Preface 1. A Process for Success 2. Linear Regression - The Blocking and Tackling of Machine Learning FREE CHAPTER 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

Business case

For this chapter, we will stick to cancer--prostate cancer in this case. It is a small dataset of 97 observations and nine variables but allows you to fully grasp what is going on with regularization techniques by allowing a comparison with traditional techniques. We will start by performing best subsets regression to identify the features and use this as a baseline for our comparison.

Business understanding

The Stanford University Medical Center has provided preoperative Prostate Specific Antigen (PSA) data on 97 patients who are about to undergo radical prostatectomy (complete prostate removal) for the treatment of prostate cancer. The American Cancer Society (ACS) estimates that nearly 30,000 American men died of prostate cancer in 2014 (http://www...

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