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The Regularization Cookbook

You're reading from   The Regularization Cookbook Explore practical recipes to improve the functionality of your ML models

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Product type Paperback
Published in Jul 2023
Publisher Packt
ISBN-13 9781837634088
Length 424 pages
Edition 1st Edition
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Author (1):
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Vincent Vandenbussche Vincent Vandenbussche
Author Profile Icon Vincent Vandenbussche
Vincent Vandenbussche
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Toc

Table of Contents (14) Chapters Close

Preface 1. Chapter 1: An Overview of Regularization 2. Chapter 2: Machine Learning Refresher FREE CHAPTER 3. Chapter 3: Regularization with Linear Models 4. Chapter 4: Regularization with Tree-Based Models 5. Chapter 5: Regularization with Data 6. Chapter 6: Deep Learning Reminders 7. Chapter 7: Deep Learning Regularization 8. Chapter 8: Regularization with Recurrent Neural Networks 9. Chapter 9: Advanced Regularization in Natural Language Processing 10. Chapter 10: Regularization in Computer Vision 11. Chapter 11: Regularization in Computer Vision – Synthetic Image Generation 12. Index 13. Other Books You May Enjoy

Training a linear regression model with scikit-learn

Linear regression is one the most basic ML models we can use, but it is very useful. Most people used linear regression in high school without talking about ML, and still use it on a regular basis within spreadsheets. In this recipe, we will explain the basics of linear regression, and then train and evaluate a linear regression model using scikit-learn on the California housing dataset.

Getting ready

Linear regression is not a complicated model, but it is still useful to understand what is under the hood to get the best out of it.

The way linear regression works is pretty straightforward. Heading back to the real estate price example, if we consider a feature x such as the apartment surface and a label y such as the apartment price, a common solution would be to find a and b such that y = ax + b.

Unfortunately, this is not so simple in real life. There is usually no a and b that makes this equality always respected....

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