You're reading from Python Machine Learning - Third Edition

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Published inDec 2019

Reading LevelExpert

PublisherPackt

ISBN-139781789955750

Edition3rd Edition

Languages

Python

Tools

TensorFlow Keras

Concepts

Machine Learning

Authors (2):

Sebastian Raschka

Vahid Mirjalili

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Handling categorical data

So far, we have only been working with numerical values. However, it is not uncommon for real-world datasets to contain one or more categorical feature columns. In this section, we will make use of simple yet effective examples to see how to deal with this type of data in numerical computing libraries.

When we are talking about categorical data, we have to further distinguish between ordinal and nominal features. Ordinal features can be understood as categorical values that can be sorted or ordered. For example, t-shirt size would be an ordinal feature, because we can define an order: XL > L > M. In contrast, nominal features don't imply any order and, to continue with the previous example, we could think of t-shirt color as a nominal feature since it typically doesn't make sense to say that, for example, red is larger than blue.

Categorical data encoding with pandas

Before we explore different techniques for handling such categorical...

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Python Machine Learning - Third Edition

Published in: Dec 2019Publisher: PacktISBN-13: 9781789955750

Authors (2)

Sebastian Raschka

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.
Read more about Sebastian Raschka

Vahid Mirjalili

Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.
Read more about Vahid Mirjalili

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Handling categorical data

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