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Hands-On Automated Machine Learning

You're reading from   Hands-On Automated Machine Learning A beginner's guide to building automated machine learning systems using AutoML and Python

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788629898
Length 282 pages
Edition 1st Edition
Languages
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Authors (2):
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Umit Mert Cakmak Umit Mert Cakmak
Author Profile Icon Umit Mert Cakmak
Umit Mert Cakmak
Sibanjan Das Sibanjan Das
Author Profile Icon Sibanjan Das
Sibanjan Das
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to AutoML 2. Introduction to Machine Learning Using Python FREE CHAPTER 3. Data Preprocessing 4. Automated Algorithm Selection 5. Hyperparameter Optimization 6. Creating AutoML Pipelines 7. Dive into Deep Learning 8. Critical Aspects of ML and Data Science Projects 9. Other Books You May Enjoy

Linearity versus non-linearity

Another consideration is decision boundaries. Some algorithms, such as logistic regression or Support Vector Machine (SVM), can learn linear decision boundaries while others, such as tree-based algorithms, can learn non-linear decision boundaries. While linear decision boundaries are relatively easy to calculate and interpret, you should be aware of errors that linear algorithms will generate in the presence of non-linear relationships.

Drawing decision boundaries

The following code snippet will allow you to examine the decision boundaries of different types of algorithms:

import matplotlib.cm as cm

# This function will scale training datatset and train given classifier.
# Based on predictions...
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