:py:mod:`sklearn_pmml_model.naive_bayes`
========================================

.. py:module:: sklearn_pmml_model.naive_bayes

.. autoapi-nested-parse::

   
   The :mod:`sklearn_pmml_model.naive_bayes` module implements Naive Bayes
   algorithms. These are supervised learning methods based on applying Bayes'
   theorem with strong (naive) feature independence assumptions.
















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

   implementations/index.rst


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

Classes
~~~~~~~

.. autoapisummary::

   sklearn_pmml_model.naive_bayes.PMMLGaussianNB




.. py:class:: PMMLGaussianNB(pmml)


   Bases: :py:obj:`sklearn_pmml_model.base.OneHotEncodingMixin`, :py:obj:`sklearn_pmml_model.base.PMMLBaseClassifier`, :py:obj:`sklearn.naive_bayes.GaussianNB`

   
   Gaussian Naive Bayes classifier.

   Can perform online updates to model parameters via :meth:`partial_fit`.
   For details on algorithm used to update feature means and variance online,
   see Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque:

       http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf

   :Parameters:

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









   .. rubric:: Notes

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





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   .. py:method:: _get_target_values(inputs, target)


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

      
      Not supported: PMML models are already fitted.
















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   .. py:method:: _more_tags()