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Python Data Analysis, Second Edition

You're reading from   Python Data Analysis, Second Edition Data manipulation and complex data analysis with Python

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781787127487
Length 330 pages
Edition 2nd Edition
Languages
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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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Table of Contents (16) Chapters Close

Preface 1. Getting Started with Python Libraries 2. NumPy Arrays FREE CHAPTER 3. The Pandas Primer 4. Statistics and Linear Algebra 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

Naive Bayes classification

Classification algorithms are a type of machine learning algorithm that determine 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 a piece...

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