The introduction of synthetic data into a training set can have both positive and negative impacts on model performance, depending on the specific context and how well the synthetic data is generated.

  1. Increase in training data: Synthetic data can help increase the size of the training set, which is often beneficial for improving model performance. With more diverse examples, the model can learn more generalized patterns and have better predictive capabilities.

  2. Enhanced robustness: By introducing synthetic data that represents edge cases or outliers, the model can become more robust and resilient to extreme situations. This can help prevent overfitting and improve the model's ability to generalize to unseen data.

  3. Addressing class imbalance: In scenarios where certain classes in the training set are underrepresented, synthetic data generation techniques can be used to balance the dataset. This can help the model better learn the characteristics of minority classes and improve performance on them.

  4. Impact on generalization: The quality of the synthetic data is crucial. If the synthetic data does not accurately represent the underlying patterns of the real data, it can introduce noise and degrade model performance. Care must be taken to ensure that the synthetic data generation methods capture the relevant features and distributions of the real data.

  5. Bias and fairness: Introducing synthetic data can inadvertently introduce biases if the synthetic data generation process is biased or if the underlying training data already contains biases. It is important to carefully evaluate and monitor the fairness and biases in the training set when incorporating synthetic data.

  6. Computational efficiency: Synthetic data generation can be computationally expensive, especially for complex models or large datasets. The additional computational cost should be considered, especially if it outweighs the potential benefits on model performance.

  Overall, the impact of introducing synthetic data into a training set on model performance is context-dependent. Proper evaluation and validation of the synthetic data generation methods are necessary to ensure that the benefits outweigh any potential drawbacks.