:py:mod:`sklearn_pmml_model.neighbors`
======================================

.. py:module:: sklearn_pmml_model.neighbors

.. autoapi-nested-parse::

   
   The :mod:`sklearn.neighbors` module implements the k-nearest neighbors
   algorithm.
















   ..
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Submodules
----------
.. toctree::
   :titlesonly:
   :maxdepth: 1

   _base/index.rst
   _classes/index.rst


Package Contents
----------------

Classes
~~~~~~~

.. autoapisummary::

   sklearn_pmml_model.neighbors.PMMLKNeighborsClassifier
   sklearn_pmml_model.neighbors.PMMLKNeighborsRegressor




.. py:class:: PMMLKNeighborsClassifier(pmml, n_jobs=None)


   Bases: :py:obj:`sklearn_pmml_model.base.PMMLBaseClassifier`, :py:obj:`sklearn_pmml_model.neighbors._base.PMMLBaseKNN`, :py:obj:`sklearn.neighbors.KNeighborsClassifier`

   
   Classifier implementing the k-nearest neighbors vote.


   :Parameters:

       **pmml** : str, object
           Filename or file object containing PMML data.

       **n_jobs** : int, default=None
           The number of parallel jobs to run for neighbors search.
           ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
           ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
           for more details.
           Doesn't affect :meth:`fit` method.









   .. rubric:: Notes

   Specification: http://dmg.org/pmml/v4-3/KNN.html





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   .. py:method:: fit(x, y)

      
      Not supported: PMML models are already fitted.
















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   .. py:method:: _more_tags()



.. py:class:: PMMLKNeighborsRegressor(pmml, n_jobs=None)


   Bases: :py:obj:`sklearn_pmml_model.base.PMMLBaseRegressor`, :py:obj:`sklearn_pmml_model.neighbors._base.PMMLBaseKNN`, :py:obj:`sklearn.neighbors.KNeighborsRegressor`

   
   Regression based on k-nearest neighbors.

   The target is predicted by local interpolation of the targets
   associated of the nearest neighbors in the training set.

   :Parameters:

       **pmml** : str, object
           Filename or file object containing PMML data.

       **n_jobs** : int, default=None
           The number of parallel jobs to run for neighbors search.
           ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
           ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
           for more details.
           Doesn't affect :meth:`fit` method.









   .. rubric:: Notes

   Specification: http://dmg.org/pmml/v4-3/KNN.html





   ..
       !! processed by numpydoc !!
   .. py:method:: fit(x, y)

      
      Not supported: PMML models are already fitted.
















      ..
          !! processed by numpydoc !!

   .. py:method:: _more_tags()