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Test Driven Machine Learning

You're reading from  Test Driven Machine Learning

Product type Book
Published in Nov 2015
Publisher
ISBN-13 9781784399085
Pages 190 pages
Edition 1st Edition
Languages

Table of Contents (16) Chapters

Test-Driven Machine Learning
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Introducing Test-Driven Machine Learning 2. Perceptively Testing a Perceptron 3. Exploring the Unknown with Multi-armed Bandits 4. Predicting Values with Regression 5. Making Decisions Black and White with Logistic Regression 6. You're So Naïve, Bayes 7. Optimizing by Choosing a New Algorithm 8. Exploring scikit-learn Test First 9. Bringing It All Together Index

Summary


In this chapter, we built up a Gaussian Naïve Bayes classifier, and ran into our first examples of truly necessary refactoring. We also saw how needing to make enormous changes in the code for a test is sometimes the result of trying to test too many concepts at once. We saw how backing up and rethinking test design can ultimately lead to a better and more elegantly designed piece of software as well.

In the next chapter, we'll apply this classifier to the real data, and see what it looks like to compare how different classifiers perform on the same data.

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