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MLSyntheticDataParameters

Source code in docs_src/capo_cleanroomsml/types/ml_synthetic_data_parameters.py 
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class MLSyntheticDataParameters(TypedDict, closed=True):
    epsilon: "float"
    """<p>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.</p>"""
    max_membership_inference_attack_score: "float"
    """<p>The maximum acceptable score for membership inference attack vulnerability. Synthetic data generation fails if the score for the resulting data exceeds this threshold.</p>"""
    column_classification: NotRequired[
        "capo_cleanroomsml.types.column_classification_details.ColumnClassificationDetails"
    ]
    """<p>Classification details for data columns that specify how each column should be treated during synthetic data generation.</p>"""

column_classification instance-attribute 

column_classification: NotRequired[ColumnClassificationDetails]

Classification details for data columns that specify how each column should be treated during synthetic data generation.

epsilon instance-attribute 

epsilon: float

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.

max_membership_inference_attack_score instance-attribute 

max_membership_inference_attack_score: float

The maximum acceptable score for membership inference attack vulnerability. Synthetic data generation fails if the score for the resulting data exceeds this threshold.