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Mastering Machine Learning with scikit-learn. - Second Edition

You're reading from  Mastering Machine Learning with scikit-learn. - Second Edition

Product type Book
Published in Jul 2017
Publisher
ISBN-13 9781788299879
Pages 254 pages
Edition 2nd Edition
Languages
Author (1):
Gavin Hackeling Gavin Hackeling
Profile icon Gavin Hackeling
Toc

Table of Contents (22) Chapters close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. The Fundamentals of Machine Learning 2. Simple Linear Regression 3. Classification and Regression with k-Nearest Neighbors 4. Feature Extraction 5. From Simple Linear Regression to Multiple Linear Regression 6. From Linear Regression to Logistic Regression 7. Naive Bayes 8. Nonlinear Classification and Regression with Decision Trees 9. From Decision Trees to Random Forests and Other Ensemble Methods 10. The Perceptron 11. From the Perceptron to Support Vector Machines 12. From the Perceptron to Artificial Neural Networks 13. K-means 14. Dimensionality Reduction with Principal Component Analysis Index

Applying linear regression


We have worked through a toy problem to learn how linear regression models relationships between explanatory and response variables. Now we'll use a real dataset and apply linear regression to an important task. Assume that you are at a party, and that you wish to drink the best wine that is available. You could ask your friends for recommendations, but you suspect that they will drink anything, regardless of its provenance. Fortunately, you have brought pH test strips and other tools for measuring various physicochemical properties—it is, after all, a party. We will use machine learning to predict the quality of wine based on its physicochemical attributes.

The UCI Machine Learning Repository's Wine dataset measures eleven physicochemical attributes, including pH and alcohol content, of 1,599 different red wines. Each wine's quality has been scored by human judges. The scores range from zero to ten; zero is the worst quality, and ten is the best quality. The dataset...

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