The Art of Feature Engineering (Paperback)

Pablo Duboue

ISBN 10: 1108709389 / ISBN 13: 9781108709385

Published by Cambridge University Press, Cambridge, 2020

About this Item

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 Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9781108709385

Bibliographic Details

• Title: The Art of Feature Engineering (Paperback)
• Publisher: Cambridge University Press, Cambridge
• Publication Date: 2020
• Language: English
• ISBN 10: 1108709389
• ISBN 13: 9781108709385
• Binding: Paperback
• Condition: new

About this title

Synopsis

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.

About the Author

Pablo Duboue is Director of Textualization Software Ltd. and is passionate about improving society through technology. He has a Ph.D. in Computer Science from Columbia University and was part of the IBM Watson team that beat the Jeopardy! Champions in 2011. He splits his time between teaching machine learning, doing open research, contributing to free software projects, and consulting for start-ups. He has taught in three different countries and done joint research with more than fifty co-authors. Recent career highlights include a best paper award in the Canadian AI conference industrial track and consulting for a start-up acquired by Intel Corp.