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The Supervised Learning Workshop

You're reading from   The Supervised Learning Workshop Predict outcomes from data by building your own powerful predictive models with machine learning in Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781800209046
Length 532 pages
Edition 2nd Edition
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Authors (4):
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Blaine Bateman Blaine Bateman
Author Profile Icon Blaine Bateman
Blaine Bateman
Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
Ishita Mathur Ishita Mathur
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Ishita Mathur
Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
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Classification Using Decision Trees

Another powerful classification method that we will be examining in this chapter is decision trees, which have found particular use in applications such as natural language processing, for example. There are a number of different machine learning algorithms that fall within the overall umbrella of decision trees, such as Iterative Dichotomiser 3 (ID3) and Classification and Regression Tree (CART). In this chapter, we will investigate the use of the ID3 method in classifying categorical data, and we will use the scikit-learn CART implementation as another method of classifying the dataset. So, what exactly are decision trees?

As the name suggests, decision trees are a learning algorithm that apply a sequential series of decisions based on input information to make the final classification. Recalling your childhood biology class, you may have used a process similar to decision trees in the classification of different types of animals via dichotomous...

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