You're reading from Scikit-learn Cookbook Over 80 recipes for machine learning in Python with scikit-learn

Product type Paperback

Published in Dec 2025

Last Updated in Sep 2025

Publisher Packt

ISBN-13 9781836644453

Length 414 pages

Edition 3rd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

John Sukup

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Table of Contents (14) Chapters

1. Scikit-learn Cookbook, Third Edition: Over 80 recipes for machine learning in Python with scikit-learn

2. Chapter 1: Common Conventions and API Elements of scikit-learn FREE CHAPTER

3. Chapter 2: Pre-Model Workflow and Data Preprocessing

4. Chapter 3: Dimensionality Reduction Techniques

5. Chapter 4: Building Models with Distance Metrics and Nearest Neighbors

6. Chapter 5: Linear Models and Regularization

7. Chapter 6: Advanced Logistic Regression and Extensions

8. Chapter 7: Support Vector Machines and Kernel Methods

9. Chapter 8: Tree-Based Algorithms and Ensemble Methods

10. Chapter 9: Text Processing and Multiclass Classification

11. Chapter 10: Clustering Techniques

12. Chapter 11: Novelty and Outlier Detection

13. Chapter 12: Cross-Validation and Model Evaluation Techniques

14. Chapter 13: Deploying scikit-learn Models in Production

Ridge and Lasso Regression

Ridge and Lasso regression are two powerful techniques used to enhance the performance of linear regression models through what’s called regularization. Regularization helps prevent overfitting by adding a penalty term to the loss function, which discourages overly complex models. We’ll discuss this in more detail later on.

In this recipe we will learn about the fundamentals of these two powerful regression techniques, how they differ from standard linear regression, and how to implement and compare these models using scikit-learn.

Getting ready

In order to implement Ridge and Lasso regression, we'll use the Ridge and Lasso classes from the sklearn.linear_model module. These classes are similar to LinearRegression(), but they include an additional parameter called alpha, which controls the strength of the regularization. We can use the same dataset created in the Introduction to Linear Models section Ito demonstrate the implementation of...