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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

Concepts

Machine Learning

Table of Contents (23) Chapters

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

Summary

In this chapter, we looked at using probabilistic linear models to predict a qualitative response with three methods: logistic regression, discriminant analysis, and MARS. Additionally, we began the process of using ROC charts in order to explore model selection visually and statistically. We also briefly discussed the model selection and trade-offs that you need to consider. In future chapters, we will revisit the breast cancer dataset to see how more complex techniques perform.

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