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

View More author details

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

ElasticNet and Regularization

ElasticNet is a hybrid of Ridge and Lasso regression which combines their strengths to handle different types of data. With ElasticNet, we can control the strength of the regularization using the alpha parameter, which is similar to the alpha parameter in Ridge and Lasso regression. We can also control the mix of Ridge and Lasso regularization using the l1_ratio parameter, which controls the proportion of Ridge and Lasso regularization in the model. This is useful when we have a dataset with a mix of features that are highly correlated with each other and some that are not. This recipe will introduce you to a technique that blends the benefits of both regularization techniques we covered previously.

Getting ready

In order to implement ElasticNet regression, we'll use the ElasticNet() class from the sklearn.linear_model module. This class is similar to Ridge and Lasso regression, but it combines their strengths to handle different types of data. We can...