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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

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781787287471

Length 420 pages

Edition 2nd Edition

Languages

R

Concepts

Machine Learning

Author (1):

Cory Lesmeister

View More author details

Table of Contents (17) Chapters

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

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.cancer.org/). PSA is a protein that is produced by the prostate gland and is found in the bloodstream. The goal is to develop a predictive model of PSA among the provided set of clinical measures. PSA can be an effective prognostic...

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Authors (1)

Lesmeister

Lesmeister

Cory Lesmeister has over fourteen years of quantitative experience and is currently a senior data scientist for the advanced analytics team at Cummins, Inc. in Columbus, Indiana. He has spent 16 years at Eli Lilly and Company in sales, market research, Lean Six Sigma, marketing analytics, and new product forecasting. He also has several years of experience in the insurance and banking industries, both as a consultant and as a manager of marketing analytics. A former US Army active duty and reserve officer, Cory was stationed in Baghdad, Iraq, in 2009. Here, he served as the strategic advisor to the 29,000-person Iraqi Oil Police, succeeding where others failed by acquiring and delivering promised equipment to help the country secure and protect its oil infrastructure. He has a BBA in aviation administration from the University of North Dakota and a commercial helicopter license.

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