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Statistics for Machine Learning

You're reading from Statistics for Machine Learning

Product type Book

Published in Jul 2017

Publisher Packt

ISBN-13 9781788295758

Pages 442 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Pratap Dangeti

Table of Contents (16) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Journey from Statistics to Machine Learning

2. Parallelism of Statistics and Machine Learning

3. Logistic Regression Versus Random Forest

4. Tree-Based Machine Learning Models

5. K-Nearest Neighbors and Naive Bayes

6. Support Vector Machines and Neural Networks

7. Recommendation Engines

8. Unsupervised Learning

9. Reinforcement Learning

Laplace estimator

In the previous calculation, all the values are nonzeros, which makes calculations well. Whereas in practice some words never appear in past for specific category and suddenly appear at later stages, which makes entire calculations as zeros.

For example, in the previous equation W₃ did have a 0 value instead of 13, and it will convert entire equations to 0 altogether:

In order to avoid this situation, Laplace estimator essentially adds a small number to each of the counts in the frequency table, which ensures that each feature has a nonzero probability of occurring with each class. Usually Laplace estimator is set to 1, which ensures that each class-feature combination is found in the data at least once:

Note

If you observe the equation carefully, value 1 is added to all three words in numerator and at the same time three has been added to all denominators to provide equivalence.

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Authors (1)

Pratap Dangeti

Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.

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