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  • Book Overview & Buying Python Feature Engineering Cookbook
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Python Feature Engineering Cookbook

Python Feature Engineering Cookbook

By : Soledad Galli
3.6 (9)
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Python Feature Engineering Cookbook

Python Feature Engineering Cookbook

3.6 (9)
By: Soledad Galli

Overview of this book

Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems.
Table of Contents (13 chapters)
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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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Python Feature Engineering Cookbook
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