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
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- Publication planned for: July 2020
- format: Paperback
- isbn: 9781108709385
- dimensions: 228 x 152 mm
- availability: Not yet published - available from July 2020
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.
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