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sdv.metrics.tabular.MLEfficacyMetric¶

class sdv.metrics.tabular.MLEfficacyMetric[source]¶

Base class for Machine Learning Efficacy metrics on single tables.

These metrics fit a Machine Learning model on the training data and then evaluate it making predictions on the test data.

name¶

Name to use when reports about this metric are printed.

Type: str

goal¶

The goal of this metric.

Type: sdmetrics.goal.Goal

min_value¶

Minimum value or values that this metric can take.

Type: Union[float, tuple[float]]

max_value¶

Maximum value or values that this metric can take.

Type: Union[float, tuple[float]]

model¶: Model class to use for the prediction.

model_kwargs¶: Keyword arguments to use to create the model instance.

__init__()¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`()	Initialize self.
`compute`(test_data, train_data[, metadata, …])	Compute this metric.
`compute_breakdown`(real_data, synthetic_data)	Compute this metric breakdown.
`get_subclasses`([include_parents])	Recursively find subclasses of this metric.
`normalize`(raw_score)	Compute the normalized value of the metric.

Attributes

`METRICS`
`MODEL`
`MODEL_KWARGS`
`goal`
`max_value`
`min_value`
`name`

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