evalml.data_checks.LabelLeakageDataCheck.validate

LabelLeakageDataCheck.validate(X, y)

Check if any of the features are highly correlated with the target.

Currently only supports binary and numeric targets and features.

Parameters

• X (pd.DataFrame) – The input features to check

• y (pd.Series) – The target data

Returns

List with a DataCheckWarning if there is label leakage detected.

Return type

list (DataCheckWarning)

Example

>>> X = pd.DataFrame({
...    'leak': [10, 42, 31, 51, 61],
...    'x': [42, 54, 12, 64, 12],
...    'y': [12, 5, 13, 74, 24],
... })
>>> y = pd.Series([10, 42, 31, 51, 40])
>>> label_leakage_check = LabelLeakageDataCheck(pct_corr_threshold=0.8)
>>> assert label_leakage_check.validate(X, y) == [DataCheckWarning("Column 'leak' is 80.0% or more correlated with the target", "LabelLeakageDataCheck")]

evalml.data_checks.LabelLeakageDataCheck.__init__ evalml.data_checks.OutliersDataCheck