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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

Product type Paperback

Published in Jul 2017

Publisher

ISBN-13 9781788299879

Length 254 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

Gavin Hackeling

View More author details

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 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

Index

Chapter 8. Nonlinear Classification and Regression with Decision Trees

In this chapter, we will discuss a simple, nonlinear model for classification and regression tasks called the decision tree. We'll use decision trees to build an ad blocker that can learn to classify images on a web page as banner advertisements or page content.While decision trees are seldom used in practice, they are components of more powerful models; understandingdecision trees is important for this reason.

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Authors (1)

Gavin Hackeling

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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