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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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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

Using logistic regression


Contrary to its name, logistic regression is a classification method, and is very powerful when it comes to text-based classification. It achieves this by first performing regression on a logistic function, hence the name.

A bit of math with a small example

To get an initial understanding of the way logistic regression works, let us first take a look at the following example, where we have an artificial feature value at the X axis plotted with the corresponding class range, either 0 or 1. As we can see, the data is so noisy that classes overlap in the feature value range between 1 and 6. Therefore, it is better to not directly model the discrete classes, but rather the probability that a feature value belongs to class 1, P(X). Once we possess such a model, we could then predict class 1 if P(X) > 0.5 or class 0 otherwise:

Mathematically, it is always difficult to model something that has a finite range, as is the case here with our discrete labels 0 and 1. We can...

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