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Danger
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.
MLPRegressor
MLPRegressor Efficacy based metric.
This fits a MLPRegressor to the training data and then evaluates it making predictions on the test data.
__init__
Initialize self. See help(type(self)) for accurate signature.
Methods
SCORER(y_pred, *[, sample_weight, …])
SCORER
\(R^2\) (coefficient of determination) regression score function.
__init__()
Initialize self.
compute(test_data, train_data[, metadata, …])
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
Return a normalized version of the R^2 score.
Attributes
METRICS
MODEL_KWARGS
goal
max_value
min_value
name