evalml.data_checks.InvalidTargetDataCheck.validate

InvalidTargetDataCheck.validate(X, y)

Checks if the target data contains missing or invalid values.

Parameters

• X (pd.DataFrame, pd.Series, np.array, list) – Features. Ignored.

• y – Target data to check for invalid values.

Returns

List with DataCheckErrors if any invalid values are found in the target data.

Return type

list (DataCheckError)

Example

>>> X = pd.DataFrame({})
>>> y = pd.Series([0, 1, None, None])
>>> target_check = InvalidTargetDataCheck('binary')
>>> assert target_check.validate(X, y) == [DataCheckError("2 row(s) (50.0%) of target values are null", "InvalidTargetDataCheck")]

evalml.data_checks.InvalidTargetDataCheck.__init__ evalml.data_checks.HighlyNullDataCheck