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

Random Forests and Bagging

While building a single decision tree model is intuitive, most real-world applications will only use them as part of an ensemble method due to some of their shortcomings – especially in overfitting. As the saying goes, “two heads (or trees in this case) are better than one!”

Cleverly named random forests are robust ensemble models that combine multiple decision trees to improve accuracy and reduce overfitting. They achieve this by employing a method known as bagging (Bootstrap Aggregating), where multiple trees are trained on different subsets of the data sampled with replacement. Each tree contributes a prediction vote, and the final prediction is based on the majority vote or average of predictions from all trees. Random forests excel in handling large datasets and complex feature interactions better than a single decision tree alone. This recipe will introduce you to ensemble methods.