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

K-means Clustering

K-means is a centroid-based clustering algorithm that partitions data into a predefined number of clusters, which is perfect considering our data is quite blobby from the Introduction to Clustering section. First, K-means randomly creates centroids in our feature space. Next, it iteratively assigns each data point to the nearest cluster centroid and then recalculates the cluster centroids and moves them in the feature space so that they are positioned approximately within the average distance among the data points current assigned to them in the current iteration. This process continues until convergence where the centroids don’t move much and data points are not being reassigned to other cluster centroid. K-means is efficient and works best when clusters are convex, isotropic, and roughly equal in size…which also can be its greatest weakness. This recipe will walk you through this process.