no_split
======================================================

.. py:module:: evalml.preprocessing.data_splitters.no_split

.. autoapi-nested-parse::

   Empty Data Splitter class.



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

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

.. autoapisummary::

   evalml.preprocessing.data_splitters.no_split.NoSplit





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: NoSplit(random_seed=0)



   Does not split the training data into training and validation sets.

   All data is passed as the training set, test data is simply an array of
   `None`. To be used for future unsupervised learning, should not be used
   in any of the currently supported pipelines.

   :param random_seed: The seed to use for random sampling. Defaults to 0. Not used.
   :type random_seed: int




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.preprocessing.data_splitters.no_split.NoSplit.get_n_splits
      evalml.preprocessing.data_splitters.no_split.NoSplit.is_cv
      evalml.preprocessing.data_splitters.no_split.NoSplit.split

   .. py:method:: get_n_splits()
      :staticmethod:

      Return the number of splits of this object.

      :returns: Always returns 0.
      :rtype: int


   .. py:method:: is_cv(self)
      :property:

      Returns whether or not the data splitter is a cross-validation data splitter.

      :returns: If the splitter is a cross-validation data splitter
      :rtype: bool


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

      Divide the data into training and testing sets, where the testing set is empty.

      :param X: Dataframe of points to split
      :type X: pd.DataFrame
      :param y: Series of points to split
      :type y: pd.Series

      :returns: Indices to split data into training and test set
      :rtype: list