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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

Remedial actions to push the model towards the ideal region

Models with either high bias or high variance error components do not produce the ideal fit. Hence, some makeovers are required to do so. In the following diagram, the various methods applied are shown in detail. In the case of linear regression, there would be a high bias component, meaning the model is not flexible enough to fit some non-linearities in data. One turnaround is to break the single line into small linear pieces and fit them into the region by constraining them at knots, also called Linear Spline. Whereas decision trees have a high variance problem, meaning even a slight change in X values leads to large changes in its corresponding Y values, this issue can be resolved by performing an ensemble of the decision trees:

In practice, implementing splines would be a difficult and not so popular method, due to the involvement of the many equations a practitioner has to keep tabs on, in addition to checking the linearity...

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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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