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

Introduction to Pipelines in scikit-learn

In ML, managing the workflow of data preprocessing and model training can become complex, especially when multiple steps are involved (which is almost always the case in the real world). The Pipeline() class in scikit-learn offers a powerful solution to streamline this process. By allowing users to chain together various preprocessing steps and model training into a single object, pipelines enhance code efficiency and reduce the likelihood of errors. This chapter will introduce readers to the concept of pipelines, demonstrating how to create and utilize them effectively for data preprocessing in scikit-learn. We’ll be utilizing pipelines throughout the book as we add more steps to our model development workflow.

What is a Pipeline?

A pipeline in scikit-learn is essentially a sequence of steps that are executed in order. Each step in the pipeline consists of a name and an associated transformer or estimator (refer to Chapter 1...