evalml.data_checks.OutliersDataCheck.validate

OutliersDataCheck.validate(X, y=None)

Checks if there are any outliers in a dataframe by using an Isolation Forest to obtain the anomaly score of each index and then using IQR to determine score anomalies. Indices with score anomalies are considered outliers.

Parameters

• X (pd.DataFrame) – features

• y – Ignored.

Returns

A set of indices that may have outlier data.

Example

>>> df = pd.DataFrame({
...     'x': [1, 2, 3, 40, 5],
...     'y': [6, 7, 8, 990, 10],
...     'z': [-1, -2, -3, -1201, -4]
... })
>>> outliers_check = OutliersDataCheck()
>>> assert outliers_check.validate(df) == [DataCheckWarning("Row '3' is likely to have outlier data", "OutliersDataCheck")]

evalml.data_checks.OutliersDataCheck.__init__ evalml.data_checks.NoVarianceDataCheck