Book description
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
Publisher resources
Table of contents
- Preface
- 1. Introducing Synthetic Data Generation
- 2. Implementing Data Synthesis
- 3. Getting Started: Distribution Fitting
- 4. Evaluating Synthetic Data Utility
- 5. Methods for Synthesizing Data
- 6. Identity Disclosure in Synthetic Data
- 7. Practical Data Synthesis
- Index
Product information
- Title: Practical Synthetic Data Generation
- Author(s):
- Release date: May 2020
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492072744
You might also like
book
Accelerating AI with Synthetic Data
Recently, data scientists have found effective methods to generate high-quality synthetic data. That’s good news for …
book
Synthetic Data for Deep Learning: Generate Synthetic Data for Decision Making and Applications with Python and R
Data is the indispensable fuel that drives the decision making of everything from governments, to major …
book
Deep Learning with Structured Data
Deep learning offers the potential to identify complex patterns and relationships hidden in data of all …
book
Practical Simulations for Machine Learning
Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, …