Other available formats:
Looking for an examination copy?
If you are interested in the title for your course we can consider offering an examination copy. To register your interest please contact email@example.com providing details of the course you are teaching.
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.Read more
- Helps the practitioner obtain better end-to-end performance by not focusing on just tuning model parameters
- Can be used as a supplementary text for machine learning or data science courses
- Presents hands-on case studies, with 200 accompanying programs for students and instructors
- Includes a first-of-its-kind publicly available dataset for teaching feature engineering
Reviews & endorsements
'Pablo Duboue is a true grandmaster of the art and science of feature engineering. His foundational contributions to the creation of IBM Watson were a critical component of its success. Now readers can benefit from his expertise. His book provides deep insights into to how to develop, assess, combine, and enhance machine learning features. Of particular interest to advanced practitioners is his discussion of feature engineering and deep learning; there is a pervasive myth in the industry that deep learning and big data have made feature engineering obsolete, but the book explains why that is often incorrect for real-world computing applications and explains the relationship between building effective features and deep neural network architectures. The book engages with countless other basic and advanced topics in the area of machine learning and feature engineering, making it a valuable resource for machine learning practitioners of all levels of experience.' J. William Murdock, IBMSee more reviews
'Feature engineering is the process of identifying, selecting and evaluating input variables to statistical and machine learning models for a given problem. Pablo Duboue's The Art of Feature Engineering introduces the process with rich detail from a practitioner’s point of view, and adds new insights through four input data scenarios for the same prediction task. Highly recommended!' Nelson Correa, Andinum Inc.
'TAoFE is a comprehensive handbook - sure to be a hit with data science practitioners. With highly accessible and didactic explanations of complex concepts, the book represents the state-of-the-art, and shows in practical terms how it applies to a wide range of real-world case studies.' Gavin Brown, University of Manchester
'This book provides a large catalogue of feature manipulation techniques along with non-trivial examples to illustrate their applicability and impact on performance. It could be suitable as a textbook for an upper level undergrad or graduate text mining or multimodal data analysis class. Recent graduates starting in field data mining and text analysis will find this a useful text.' Wlodek Zadrozny, University of North Carolina
Not yet reviewed
Be the first to review
Review was not posted due to profanity×
- Date Published: July 2020
- format: Paperback
- isbn: 9781108709385
- length: 284 pages
- dimensions: 228 x 152 x 16 mm
- weight: 0.42kg
- availability: In stock
Table of Contents
Part I. Fundamentals:
2. Features, combined
3. Features, expanded
4. Features, reduced
5. Advanced topics
Part II. Case Studies:
6. Graph data
7. Timestamped data
8. Textual data
9. Image data
10. Other domains.
Find resources associated with this titleYour search for '' returned .
Type Name Unlocked * Format Size
This title is supported by one or more locked resources. Access to locked resources is granted exclusively by Cambridge University Press to instructors whose faculty status has been verified. To gain access to locked resources, instructors should sign in to or register for a Cambridge user account.
Please use locked resources responsibly and exercise your professional discretion when choosing how you share these materials with your students. Other instructors may wish to use locked resources for assessment purposes and their usefulness is undermined when the source files (for example, solution manuals or test banks) are shared online or via social networks.
Supplementary resources are subject to copyright. Instructors are permitted to view, print or download these resources for use in their teaching, but may not change them or use them for commercial gain.
If you are having problems accessing these resources please contact firstname.lastname@example.org.
Sorry, this resource is locked
Please register or sign in to request access. If you are having problems accessing these resources please email email@example.comRegister Sign in
You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.Continue ×
Are you sure you want to delete your account?
This cannot be undone.
Thank you for your feedback which will help us improve our service.
If you requested a response, we will make sure to get back to you shortly.×