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Scala for Machine Learning

You're reading from Scala for Machine Learning

Product type Book

Published in Dec 2014

Publisher

ISBN-13 9781783558742

Pages 624 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Patrick R. Nicolas

Table of Contents (20) Chapters

Scala for Machine Learning

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

1. Getting Started

2. Hello World!

3. Data Preprocessing

4. Unsupervised Learning

5. Naïve Bayes Classifiers

6. Regression and Regularization

7. Sequential Data Models

8. Kernel Models and Support Vector Machines

9. Artificial Neural Networks

10. Genetic Algorithms

11. Reinforcement Learning

12. Scalable Frameworks

Basic Concepts

Index

Naïve Bayes and text mining

The multinomial Naïve Bayes classifier is particularly suited for text mining. The Naïve Bayes formula is quite effective to classify the following entities:

E-mail spams
Business news stories
Movie reviews
Technical papers as per field of expertise

This third use case consists of predicting the direction of a stock given the financial news. There are two type of news that affect the stock of a particular company:

Macro trends: Economic or social news such as conflicts, economic trends, or labor market statistics
Micro updates: Financial or market news related to a specific company such as earnings, change in ownership, or press releases

Macroeconomic news related to a specific company have the potential to affect the sentiments of investors toward the company and may lead to a sudden shift in the price of its stock. Another important feature is the average time it takes for investors to react to the news and affect the price of the stock.

Long-term investors may react...

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Authors (1)

Patrick R. Nicolas

Patrick R. Nicolas is the director of engineering at Agile SDE, California. He has more than 25 years of experience in software engineering and building applications in C++, Java, and more recently in Scala/Spark, and has held several managerial positions. His interests include real-time analytics, modeling, and the development of nonlinear models.

See other products by Patrick R. Nicolas

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