Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data—fake data generated from real data—so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue.
Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution.
This book describes:
- Steps for generating synthetic data using multivariate normal distributions
- Methods for distribution fitting covering different goodness-of-fit metrics
- How to replicate the simple structure of original data
- An approach for modeling data structure to consider complex relationships
- Multiple approaches and metrics you can use to assess data utility
- How analysis performed on real data can be replicated with synthetic data
- Privacy implications of synthetic data and methods to assess identity disclosure
About the Author
Dr. Khaled El Emam is a senior scientist at the Children’s Hospital of Eastern Ontario (CHEO) Research Institute and Director of the multi-disciplinary Electronic Health Information Laboratory, conducting academic research on synthetic data generation methods, and re- identification risk measurement, and he is also a Professor in the Faculty of Medicine (Pediatrics) at the University of Ottawa.
Product details
- Publisher : O’Reilly Media; 1st edition (June 9, 2020)
- Language : English
- Paperback : ۱۶۶ pages
- ISBN-10 : ۱۴۹۲۰۷۲۷۴۵
- ISBN-13 : ۹۷۸-۱۴۹۲۰۷۲۷۴۴
- Item Weight : ۹.۶ ounces
- # Computer Vision & Pattern Recognition
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