Synthetic datasets are a collection of data artificially generated rather than being collected from real-world observations or measurements. They are created using algorithms, simulations, or mathematical models to mimic the characteristics and patterns of real data. Synthetic datasets are a valuable tool to generate data for experimentation, testing, and development when obtaining real data is challenging, costly, or undesirable.
You might want to generate synthetic datasets for several reasons:
Cost Efficiency: Creating synthetic data can be more cost-effective and efficient than collecting large volumes of real data, especially in resource-constrained environments.
Data Augmentation: Synthetic datasets allow users to augment their real-world data with variations, which can improve model robustness and performance.
Data Diversity: Synthetic datasets enable the inclusion of uncommon or rare scenarios, enriching model training with a wider range of potential inputs.
Privacy and Security: When dealing with sensitive data, synthetic datasets provide a way to train models without exposing real information, enhancing privacy and security.