Exploring edge cases:
- Create scenarios that are rare or difficult to capture in real data
- Improves model robustness and handling of unusual situations
Reducing bias:
- Carefully generated synthetic data can help mitigate biases present in real-world data
- Allows for more diverse and representative training sets
Cost-effective data acquisition:
- Generating synthetic data can be cheaper and faster than collecting real-world data
- Enables rapid prototyping and testing of models
Handling concept drift:
- Simulate future scenarios or changing conditions
- Helps prepare models for evolving environments
Improving model generalization:
- Exposing models to a wider range of scenarios than available in real data
- Can lead to better performance on unseen data
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