synthetic-data-generation

The process of creating artificial data that mimics real-world data for training machine learning models.

Synthetic data generation is useful for scenarios where real data is scarce, sensitive, or expensive to obtain. Techniques include simulations and generative models like GANs. A common misconception is that synthetic data is always inferior to real data; in many cases, well-crafted synthetic data can be as valuable and sometimes even more useful, especially for testing and training purposes.