However, it's important to note some potential drawbacks:
Quality concerns:
- Synthetic data must accurately reflect real-world patterns and relationships
- Poor-quality synthetic data can introduce new biases or errors
Validation challenges:
- Models trained on synthetic data still need thorough validation on real data
- Ensuring synthetic data truly represents real-world complexity can be difficult
Overreliance risks:
- Exclusive use of synthetic data may lead to models that don't fully capture real-world nuances
- Best used in combination with real data when possible
Generation complexity:
- Creating high-quality synthetic data can be a complex task requiring expertise
- May need sophisticated algorithms or domain knowledge to generate realistic data