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Building Machine Learning Systems with Python

You're reading from   Building Machine Learning Systems with Python Expand your Python knowledge and learn all about machine-learning libraries in this user-friendly manual. ML is the next big breakthrough in technology and this book will give you the head-start you need.

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Product type Paperback
Published in Jul 2013
Publisher Packt
ISBN-13 9781782161400
Length 290 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (20) Chapters Close

Building Machine Learning Systems with Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with Python Machine Learning FREE CHAPTER 2. Learning How to Classify with Real-world Examples 3. Clustering – Finding Related Posts 4. Topic Modeling 5. Classification – Detecting Poor Answers 6. Classification II – Sentiment Analysis 7. Regression – Recommendations 8. Regression – Recommendations Improved 9. Classification III – Music Genre Classification 10. Computer Vision – Pattern Recognition 11. Dimensionality Reduction 12. Big(ger) Data Where to Learn More about Machine Learning Index

Chapter 5. Classification – Detecting Poor Answers

Now that we are able to extract useful features from text, we can take on the challenge of building a classifier using real data. Let's go back to our imaginary website in Chapter 3, Clustering – Finding Related Posts, where users can submit questions and get them answered.

A continuous challenge for owners of these Q&A sites is to maintain a decent level of quality in the posted content. Websites such as stackoverflow.com take considerable efforts to encourage users to score questions and answers with badges and bonus points. Higher quality content is the result, as users are trying to spend more energy on carving out the question or crafting a possible answer.

One particular successful incentive is the possibility for the asker to flag one answer to their question as the accepted answer (again, there are incentives for the asker to flag such answers). This will result in more score points for the author of the flagged answer.

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