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You're reading from Machine Learning with R R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required ‚Äì this book will take you methodically through every stage of applying machine learning.

Product type Paperback

Published in Oct 2013

Publisher Packt

ISBN-13 9781782162148

Length 396 pages

Edition 1st Edition

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Machine Learning

Author (1):

Brett Lantz

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Table of Contents (19) Chapters

Machine Learning with R

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About the Author

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Preface

1. Introducing Machine Learning FREE CHAPTER

2. Managing and Understanding Data

3. Lazy Learning – Classification Using Nearest Neighbors

4. Probabilistic Learning – Classification Using Naive Bayes

5. Divide and Conquer – Classification Using Decision Trees and Rules

6. Forecasting Numeric Data – Regression Methods

7. Black Box Methods – Neural Networks and Support Vector Machines

8. Finding Patterns – Market Basket Analysis Using Association Rules

9. Finding Groups of Data – Clustering with k-means

10. Evaluating Model Performance

11. Improving Model Performance

12. Specialized Machine Learning Topics

Index

Understanding regression

Regression is concerned with specifying the relationship between a single numeric dependent variable (the value to be predicted) and one or more numeric independent variables (the predictors). We'll begin by assuming that the relationship between the independent and dependent variables follows a straight line.

Tip

The origin of the term "regression" to describe the process of fitting lines to data is rooted in a study of genetics by Sir Francis Galton in the late 19th century. Galton discovered that fathers that were extremely short or extremely tall tended to have sons whose heights were closer to average. He called this phenomenon "regression to the mean".

You might recall from algebra that lines can be defined in a slope-intercept form similar to y = a + bx, where y is the dependent variable and x is the independent variable. In this formula, the slope b indicates how much the line rises for each increase in x. The variable a indicates the value of y when x = 0...

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You're reading from Machine Learning with R R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required ‚Äì this book will take you methodically through every stage of applying machine learning.

Table of Contents (19) Chapters

Understanding regression

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You're reading from Machine Learning with R R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required ‚Äì this book will take you methodically through every stage of applying machine learning.

Table of Contents (19) Chapters

Understanding regression

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