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Mastering Machine Learning with scikit-learn

You're reading from   Mastering Machine Learning with scikit-learn Apply effective learning algorithms to real-world problems using scikit-learn

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Product type Paperback
Published in Jul 2017
Publisher
ISBN-13 9781788299879
Length 254 pages
Edition 2nd Edition
Languages
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Author (1):
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Gavin Hackeling Gavin Hackeling
Author Profile Icon Gavin Hackeling
Gavin Hackeling
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Table of Contents (15) Chapters Close

Preface 1. The Fundamentals of Machine Learning FREE CHAPTER 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

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...

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