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Machine Learning with scikit-learn Quick Start Guide

You're reading from   Machine Learning with scikit-learn Quick Start Guide Classification, regression, and clustering techniques in Python

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781789343700
Length 172 pages
Edition 1st Edition
Languages
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Author (1):
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Kevin Jolly Kevin Jolly
Author Profile Icon Kevin Jolly
Kevin Jolly
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Table of Contents (10) Chapters Close

Preface 1. Introducing Machine Learning with scikit-learn FREE CHAPTER 2. Predicting Categories with K-Nearest Neighbors 3. Predicting Categories with Logistic Regression 4. Predicting Categories with Naive Bayes and SVMs 5. Predicting Numeric Outcomes with Linear Regression 6. Classification and Regression with Trees 7. Clustering Data with Unsupervised Machine Learning 8. Performance Evaluation Methods 9. Other Books You May Enjoy

Classification trees

Classification trees are used to predict a category or class. This is similar to the classification algorithms that you have learned about previously in this book, such as the k-nearest neighbors algorithm or logistic regression.

Broadly speaking, there are three tree based algorithms that are used to solve classification problems:

  • The decision tree classifier
  • The random forest classifier
  • The AdaBoost classifier

In this section, you will learn how each of these tree based algorithms works, in order to classify a row of data as a particular class or category.

The decision tree classifier

The decision tree is the simplest tree based algorithm, and serves as the foundation for the other two algorithms....

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