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MATLAB for Machine Learning

You're reading from   MATLAB for Machine Learning Practical examples of regression, clustering and neural networks

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781788398435
Length 382 pages
Edition 1st Edition
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Authors (2):
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Pavan Kumar Kolluru Pavan Kumar Kolluru
Author Profile Icon Pavan Kumar Kolluru
Pavan Kumar Kolluru
Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (10) Chapters Close

Preface 1. Getting Started with MATLAB Machine Learning FREE CHAPTER 2. Importing and Organizing Data in MATLAB 3. From Data to Knowledge Discovery 4. Finding Relationships between Variables - Regression Techniques 5. Pattern Recognition through Classification Algorithms 6. Identifying Groups of Data Using Clustering Methods 7. Simulation of Human Thinking - Artificial Neural Networks 8. Improving the Performance of the Machine Learning Model - Dimensionality Reduction 9. Machine Learning in Practice

Polynomial regression

The linear model also includes polynomial regression, in which some predictors appear in degrees equal to or greater than 2. The model continues to be linear in the parameters. For example, a second-degree parabolic regression model looks like this:

This model can easily be estimated by introducing the second-degree term in the regression model. The difference is that in polynomial regression, the equation produces a curved line, not a straight line. Polynomial regression is usually used when the relationship between the variables looks curved. A simple curve can sometimes be straightened out by transforming one or both of the variables. A more complicated curve, however, is best handled by polynomial regression.

More generally, a polynomial regression equation assumes the following form:

In the next example, we will only deal with the case of...

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