Synthetic Data

Synthetic data is artificially generated data that is used to mimic real-world data. Synthetic data is often used for testing and training machine learning models, for benchmarking and performance evaluation, and protecting sensitive data in applications such as healthcare and finance.

It is generated using algorithms that model real-world data’s statistical and structural properties. This allows synthetic data to closely resemble real-world data regarding its distribution, relationships, and patterns while still being wholly artificial and not containing any sensitive information. Synthetic data can generate large amounts of data quickly, and bypass the privacy and security risks associated with using real-world data.It is important to evaluate the use of synthetic data carefully and to understand its limitations in various applications.