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Mastering Machine Learning with scikit-learn

You're reading from   Mastering Machine Learning with scikit-learn Apply effective learning algorithms to real-world problems using scikit-learn

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Product type Paperback
Published in Jul 2017
Publisher
ISBN-13 9781788299879
Length 254 pages
Edition 2nd Edition
Languages
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Author (1):
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Gavin Hackeling Gavin Hackeling
Author Profile Icon Gavin Hackeling
Gavin Hackeling
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Table of Contents (15) Chapters Close

Preface 1. The Fundamentals of Machine Learning FREE CHAPTER 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

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...

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