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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

Multiple linear regression


We previously trained and evaluated a model for predicting the price of a pizza. While you are eager to demonstrate the pizza price predictor to your friends and coworkers, you are concerned by the model's imperfect R-squared score and the embarrassment its predictions could cause you. How can you improve the model?

Recalling your personal pizza-eating experience; you might have some intuitions about other attributes of a pizza that are related to its price. For instance, the price often depends on the number of toppings on the pizza. Fortunately, your pizza journal describes toppings in detail; let's add the number of toppings to our training data as a second explanatory variable. We cannot proceed with simple linear regression, but we can use a generalization of simple linear regression that can use multiple explanatory variables called multiple linear regression. Multiple linear regression is given by the following model:

Whereas simple linear regression uses...

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