default_data_checks
================================================

.. py:module:: evalml.data_checks.default_data_checks

.. autoapi-nested-parse::

   A default set of data checks that can be used for a variety of datasets.



Module Contents
---------------

Classes Summary
~~~~~~~~~~~~~~~

.. autoapisummary::

   evalml.data_checks.default_data_checks.DefaultDataChecks





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: DefaultDataChecks(problem_type, objective, n_splits=3, problem_configuration=None)



   A collection of basic data checks that is used by AutoML by default.

   Includes:

       - `NullDataCheck`
       - `HighlyNullRowsDataCheck`
       - `IDColumnsDataCheck`
       - `TargetLeakageDataCheck`
       - `InvalidTargetDataCheck`
       - `NoVarianceDataCheck`
       - `ClassImbalanceDataCheck` (for classification problem types)
       - `TargetDistributionDataCheck` (for regression problem types)
       - `DateTimeFormatDataCheck` (for time series problem types)
       - 'TimeSeriesParametersDataCheck' (for time series problem types)
       - `TimeSeriesSplittingDataCheck` (for time series classification problem types)

   :param problem_type: The problem type that is being validated. Can be regression, binary, or multiclass.
   :type problem_type: str
   :param objective: Name or instance of the objective class.
   :type objective: str or ObjectiveBase
   :param n_splits: The number of splits as determined by the data splitter being used. Defaults to 3.
   :type n_splits: int
   :param problem_configuration: Required for time series problem types. Values should be passed in for time_index,
   :type problem_configuration: dict
   :param gap:
   :param forecast_horizon:
   :param and max_delay.:




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.data_checks.default_data_checks.DefaultDataChecks.validate

   .. py:method:: validate(self, X, y=None)

      Inspect and validate the input data against data checks and returns a list of warnings and errors if applicable.

      :param X: The input data of shape [n_samples, n_features]
      :type X: pd.DataFrame, np.ndarray
      :param y: The target data of length [n_samples]
      :type y: pd.Series, np.ndarray

      :returns: Dictionary containing DataCheckMessage objects
      :rtype: dict