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sdv.metrics.tabular.SVCDetection.compute

classmethod SVCDetection.compute(real_data, synthetic_data, metadata=None)

Compute this metric.

This builds a Machine Learning Classifier that learns to tell the synthetic data apart from the real data, which later on is evaluated using Cross Validation.

The output of the metric is one minus the average ROC AUC score obtained.

Parameters

  • real_data (Union[numpy.ndarray, pandas.DataFrame]) – The values from the real dataset.

  • synthetic_data (Union[numpy.ndarray, pandas.DataFrame]) – The values from the synthetic dataset.

  • metadata (dict) – Table metadata dict. If not passed, it is build based on the real_data fields and dtypes.

Returns

One minus the ROC AUC Cross Validation Score obtained by the classifier.

Return type

float

sdv.metrics.tabular.SVCDetection.get_subclasses sdv.metrics.tabular.MLEfficacyMetric