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The Applied Artificial Intelligence Workshop

You're reading from   The Applied Artificial Intelligence Workshop Start working with AI today, to build games, design decision trees, and train your own machine learning models

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781800205819
Length 420 pages
Edition 1st Edition
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Authors (3):
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Anthony So Anthony So
Author Profile Icon Anthony So
Anthony So
Zsolt Nagy Zsolt Nagy
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Zsolt Nagy
William So William So
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William So
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Toc

Table of Contents (8) Chapters Close

Preface
1. Introduction to Artificial Intelligence 2. An Introduction to Regression FREE CHAPTER 3. An Introduction to Classification 4. An Introduction to Decision Trees 5. Artificial Intelligence: Clustering 6. Neural Networks and Deep Learning Appendix

Polynomial and Support Vector Regression

When performing a polynomial regression, the relationship between x and y, or using their other names, features, and labels, is not a linear equation, but a polynomial equation. This means that instead of the 29 equation, we can have multiple coefficients and multiple powers of x in the equation.

To make matters even more complicated, we can perform polynomial regression using multiple variables, where each feature may have coefficients multiplying different powers of the feature.

Our task is to find a curve that best fits our dataset. Once polynomial regression is extended to multiple variables, we will learn the SVM model to perform polynomial regression.

Polynomial Regression with One Variable

As a recap, we have performed two types of regression so far:

  • Simple linear regression: 30
  • Multiple linear regression: 31

We will now learn how to do polynomial linear regression with one variable. The equation for polynomial...

You have been reading a chapter from
The Applied Artificial Intelligence Workshop
Published in: Jul 2020
Publisher: Packt
ISBN-13: 9781800205819
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