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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. Statistics and Linear Algebra 4. pandas Primer 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources
Index

Naive Bayes classification


Classification algorithms are a type of machine learning algorithm that involve determining the class (category or type) of a given item. For instance, we could try to determine the genre of a movie based on some features. In this case, the genre is the class to be predicted. In the next chapter, Chapter 10, Predictive Analytics and Machine Learning, we will continue with an overview of machine learning. In the meantime, we will discuss a popular algorithm called Naive Bayes classification, which is frequently used to analyze text documents.

Naive Bayes classification is a probabilistic algorithm based on the Bayes theorem from probability theory and statistics. The Bayes theorem formulates how to discount the probability of an event based on new evidence. For example, imagine that we have a bag with pieces of chocolate and other items we can't see. We will call the probability of drawing a piece of dark chocolate P(D). We will denote the probability of drawing...

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