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 Dimensionality Reduction

Dimensionality reduction is fundamental in developing robust ML pipelines on par with data preprocessing explored in Chapter 2. It involves reducing the number of features (or dimensions) in a dataset while retaining as much relevant information as possible. There are several reasons for performing this task including simplifying data, reducing computational costs, and enhancing model performance. Early in many data scientists’ careers, they are keen to throw as much data as available into model training, but even though more data is better in some regards, if that data isn’t related to our problem domain or doesn’t improve our model, we are simply adding in garbage (remember, garbage in, garbage out)! So, why is dimensionality reduction one of the most important tasks in data preprocessing? Let’s look more closely.