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Python Feature Engineering Cookbook

You're reading from   Python Feature Engineering Cookbook Over 70 recipes for creating, engineering, and transforming features to build machine learning models

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781789806311
Length 372 pages
Edition 1st Edition
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Author (1):
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Soledad Galli Soledad Galli
Author Profile Icon Soledad Galli
Soledad Galli
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Toc

Table of Contents (13) Chapters Close

Preface 1. Foreseeing Variable Problems When Building ML Models 2. Imputing Missing Data FREE CHAPTER 3. Encoding Categorical Variables 4. Transforming Numerical Variables 5. Performing Variable Discretization 6. Working with Outliers 7. Deriving Features from Dates and Time Variables 8. Performing Feature Scaling 9. Applying Mathematical Computations to Features 10. Creating Features with Transactional and Time Series Data 11. Extracting Features from Text Variables 12. Other Books You May Enjoy

Creating features with bag-of-words and n-grams

A bag-of-words (BoW), is a simplified representation of a text that captures the words that are present in the text and the number of times each word appears in the text. So, for the text string Dogs like cats, but cats do not like dogs, the derived BoW is as follows:

dogs like cats but do not
2 2 2 1 1 1

Here, each word becomes a variable, and the value of the variable represents the number of times the word appears in the string. As you can see, BoW captures multiplicity but does not retain word order or grammar. That is why it is a simple, yet useful, way of extracting features and capturing some information about the texts we are working with.

To capture some syntax, BoW can be used together with n-grams. An n-gram is a contiguous sequence of n items in a given text. Continuing with the sentence Dogs like cats, but...

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