The Art of Feature Engineering (Paperback)

Pablo Duboue

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Language: English

Published by Cambridge University Press, Cambridge, 2020

1108709389 / 9781108709385

Series: Book 1 of 1 - Essentials for Machine Learning

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Paperback. When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks. This is a guide for data scientists who want to use feature engineering to improve the performance of their machine learning solutions. The book provides a unified view of the field, beginning with basic concepts and techniques, followed by a cross-domain approach to advanced topics, like texts and images, with hands-on case studies. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

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Title
The Art of Feature Engineering (Paperback)
Author
Pablo Duboue
Publisher
Cambridge University Press, Cambridge
Publication year
2020
Condition
new
Binding
Paperback
Language
English
ISBN 10
1108709389
ISBN 13
9781108709385
Series
Book 1 of 1: Essentials for Machine Learning

CitiRetail

Stevenage, United Kingdom

5-star seller

AbeBooks seller since June 29, 2022

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