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Machine Learning with R

You're reading from   Machine Learning with R Expert techniques for predictive modeling to solve all your data analysis problems

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Product type Paperback
Published in Jul 2015
Publisher Packt
ISBN-13 9781784393908
Length 452 pages
Edition 2nd Edition
Languages
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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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Toc

Table of Contents (14) Chapters Close

Preface 1. Introducing Machine Learning 2. Managing and Understanding Data FREE CHAPTER 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

Chapter 6. Forecasting Numeric Data – Regression Methods

Mathematical relationships help us to understand many aspects of everyday life. For example, body weight is a function of one's calorie intake, income is often related to education and job experience, and poll numbers help us estimate a presidential candidate's odds of being re-elected.

When such relationships are expressed with exact numbers, we gain additional clarity. For example, an additional 250 kilocalories consumed daily may result in nearly a kilogram of weight gain per month; each year of job experience may be worth an additional $1,000 in yearly salary; and a president is more likely to be re-elected when the economy is strong. Obviously, these equations do not perfectly fit every situation, but we expect that they are reasonably correct, on average.

This chapter extends our machine learning toolkit by going beyond the classification methods covered previously and introducing techniques for estimating...

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