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Machine Learning with R

You're reading from   Machine Learning with R Expert techniques for predictive modeling

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781788295864
Length 458 pages
Edition 3rd Edition
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Author (1):
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Brett Lantz Brett Lantz
Author Profile Icon Brett Lantz
Brett Lantz
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Table of Contents (16) Chapters Close

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 Other Books You May Enjoy
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Index

Understanding regression

Regression involves specifying the relationship between a single numeric dependent variable (the value to be predicted) and one or more numeric independent variables (the predictors). As the name implies, the dependent variable depends upon the value of the independent variable or variables. The simplest forms of regression assume that the relationship between the independent and dependent variables follows a straight line.

Note

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. He discovered that fathers who were extremely short or tall tended to have sons whose heights were closer to the average height. He called this phenomenon "regression to the mean."

You might recall from basic algebra that lines can be defined in a slope-intercept form similar to y = a + bx. In this form, the letter y indicates the dependent variable...

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