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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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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

Implementing term frequency-inverse document frequency

Term Frequency-Inverse Document Frequency (TF-IDF) is a numerical statistic that captures how relevant a word is in a document, with respect to the entire collection of documents. What does this mean? Some words will appear a lot within a text document as well as across documents, for example, the English words the, a, and is. These words generally convey little information about the actual content of the document and don't make it stand out of the crowd. TF-IDF provides a way to weigh the importance of a word, by contemplating how many times it appears in a document, with respect to how often it appears across documents. Hence, commonly occurring words such as the, a, and is will have a low weight, and words more specific to a topic, such as leopard, will have a higher weight.

TF-IDF is the product...

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