How do we evaluate synthetic data for machine learning?

How do we evaluate synthetic data for machine learning?

Evaluating synthetic data involves assessing its quality, diversity, and how well it represents real-world scenarios.

What metrics should I use to assess the quality of synthetic data?

Common metrics include statistical similarity, distribution comparison, and model performance on tasks using the synthetic data.

Can synthetic data replace real data in training models?

It can, but it often depends on the application. Synthetic data should complement real data to ensure robustness.

What are the risks of using synthetic data?

Risks include potential biases, lack of representativeness, and overfitting if the synthetic data is not well-designed.