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Mastering Machine Learning with scikit-learn. - Second Edition

You're reading from Mastering Machine Learning with scikit-learn. - Second Edition

Product type Book

Published in Jul 2017

Publisher

ISBN-13 9781788299879

Pages 254 pages

Edition 2nd Edition

Languages

Concepts

Machine Learning

Author (1):

Gavin Hackeling

Table of Contents (22) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. The Fundamentals of Machine Learning

2. Simple Linear Regression

3. Classification and Regression with k-Nearest Neighbors

4. Feature Extraction

5. From Simple Linear Regression to Multiple Linear Regression

6. From Linear Regression to Logistic Regression

7. Naive Bayes

8. Nonlinear Classification and Regression with Decision Trees

9. From Decision Trees to Random Forests and Other Ensemble Methods

10. The Perceptron

11. From the Perceptron to Support Vector Machines

12. From the Perceptron to Artificial Neural Networks

13. K-means

14. Dimensionality Reduction with Principal Component Analysis

Index

Limitations of the perceptron

The perceptron uses a hyperplane to separate the positive and negative classes. A simple example of a classification problem that is linearly inseparable is the logical exclusive disjunction, or XOR. The output of XOR is 1 when one of its inputs is equal to 1 and the other is equal to 0. Otherwise, the output is 0. The inputs and outputs of XOR are plotted in two dimensions in the following graph. When XOR outputs 1, the instance is marked with a circle; when XOR outputs 0, the instance is marked with a diamond:

It is impossible to separate the circles from the diamonds using a single straight line. Suppose that the instances are pegs on a board. If you were to stretch a rubber band around both of the positive instances, and stretch a second rubber band around both of the negative instances, the bands would intersect in the middle of the board. The rubber bands represent convex hulls, or the envelope that contains all the points within the set and all the points...

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Authors (1)

Gavin Hackeling

Gavin Hackeling develops machine learning services for large-scale documents and image classification at an advertising network in New York. He received his Master's degree from New York University's Interactive Telecommunications Program, and his Bachelor's degree from the University of North Carolina.

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