MLSyntheticDataParameters
Parameters that control the generation of synthetic data for custom model training, including privacy settings and column classification details.
Contents
- columnClassification
-
Classification details for data columns that specify how each column should be treated during synthetic data generation.
Type: ColumnClassificationDetails object
Required: Yes
- epsilon
-
The epsilon value for differential privacy, which controls the privacy-utility tradeoff in synthetic data generation. Lower values provide stronger privacy guarantees but may reduce data utility.
Type: Double
Valid Range: Minimum value of 0.0001. Maximum value of 10.
Required: Yes
- maxMembershipInferenceAttackScore
-
The maximum acceptable score for membership inference attack vulnerability. Synthetic data generation fails if the score for the resulting data exceeds this threshold.
Type: Double
Valid Range: Minimum value of 0.5. Maximum value of 1.
Required: Yes
See Also
For more information about using this API in one of the language-specific AWS SDKs, see the following:
