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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

Maximum margin classification and support vectors

The following figure depicts instances from two linearly separable classes and three possible decision boundaries. All of the decision boundaries separate the training instances of the positive class from the training instances of the negative class, and a perceptron can learn any of them. Which of these decision boundaries is most likely to perform best on test data?

From this visualization, it is intuitive that the dotted decision boundary is the best. The solid decision boundary is near many of the positive instances. The test set could contain a positive instance that has a slightly smaller value for the first explanatory variable, x₁; this instance would be classified incorrectly. The dashed decision boundary is farther away from most of the training instances; however, it is near one of the positive instances and one of the negative instances.

The previous figure provides a different perspective on evaluating decision boundaries. Assume...

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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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