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You are looking at the documentation for an older version of the SDV! We are no longer supporting or maintaining this version of the software
Click here to go to the new docs pages.
sdv.metrics.tabular.
BNLikelihood
BayesianNetwork Likelihood Single Table metric.
This metric fits a BayesianNetwork to the real data and then evaluates how likely it is that the synthetic data belongs to the same distribution.
The output is the average probability across all the synthetic rows.
name
Name to use when reports about this metric are printed.
str
goal
The goal of this metric.
sdmetrics.goal.Goal
min_value
Minimum value or values that this metric can take.
Union[float, tuple[float]]
max_value
Maximum value or values that this metric can take.
__init__
Initialize self. See help(type(self)) for accurate signature.
Methods
__init__()
Initialize self.
compute(real_data, synthetic_data[, …])
compute
Compute this metric.
compute_breakdown(real_data, synthetic_data)
compute_breakdown
Compute this metric breakdown.
get_subclasses([include_parents])
get_subclasses
Recursively find subclasses of this metric.
normalize(raw_score)
normalize
Compute the normalized value of the metric.
Attributes