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scikit-learn Cookbook , Second Edition

You're reading from scikit-learn Cookbook , Second Edition Over 80 recipes for machine learning in Python with scikit-learn

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781787286382

Length 374 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

Trent Hauck

View More author details

Table of Contents (19) Chapters

Title Page

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. High-Performance Machine Learning – NumPy FREE CHAPTER

2. Pre-Model Workflow and Pre-Processing

3. Dimensionality Reduction

4. Linear Models with scikit-learn

5. Linear Models – Logistic Regression

6. Building Models with Distance Metrics

7. Cross-Validation and Post-Model Workflow

8. Support Vector Machines

9. Tree Algorithms and Ensembles

10. Text and Multiclass Classification with scikit-learn

11. Neural Networks

12. Create a Simple Estimator

Optimizing the number of centroids

When doing k-means clustering, we really do not know the right number of clusters in advance, so finding this out is an important step. Once we know (or estimate) the number of centroids, the problem will start to look more like a classification one as our knowledge to work with will have increased substantially.

Getting ready

Evaluating the model performance for unsupervised techniques is a challenge. Consequently, sklearn has several methods for evaluating clustering when a ground truth is known, and very few for when it isn't.

We'll start with a single cluster model and evaluate its similarity. This is more for the purpose of mechanics as measuring the similarity of one cluster count is clearly not useful in finding the ground truth number of clusters.

How to do it...

To get started, we'll create several blobs that can be used to simulate clusters of data:

from sklearn.datasets import make_blobs
import numpy as np
blobs, classes = make_blobs(500, centers=3...

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Authors (1)

Trent Hauck

Trent Hauck

Trent Hauck is a data scientist living and working in the Seattle area. He grew up in Wichita, Kansas and received his undergraduate and graduate degrees from the University of Kansas. He is the author of the book Instant Data Intensive Apps with pandas How-to, Packt Publishing—a book that can get you up to speed quickly with pandas and other associated technologies.

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