:py:mod:`sklearn_pmml_model.ensemble`
=====================================

.. py:module:: sklearn_pmml_model.ensemble

.. autoapi-nested-parse::

   
   The :mod:`sklearn_pmml_model.ensemble` module includes ensemble-based methods for
   classification, regression and anomaly detection.
















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

   forest/index.rst
   gb/index.rst


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

Classes
~~~~~~~

.. autoapisummary::

   sklearn_pmml_model.ensemble.PMMLForestClassifier
   sklearn_pmml_model.ensemble.PMMLForestRegressor
   sklearn_pmml_model.ensemble.PMMLGradientBoostingClassifier
   sklearn_pmml_model.ensemble.PMMLGradientBoostingRegressor




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


   Bases: :py:obj:`sklearn_pmml_model.base.IntegerEncodingMixin`, :py:obj:`sklearn_pmml_model.base.PMMLBaseClassifier`, :py:obj:`sklearn.ensemble.RandomForestClassifier`

   
   A random forest classifier.

   A random forest is a meta estimator that fits a number of decision tree
   classifiers on various sub-samples of the dataset and uses averaging to
   improve the predictive accuracy and control over-fitting.

   The PMML model consists out of a <Segmentation> element, that contains
   various <Segment> elements. Each segment contains it's own <TreeModel>.
   For Random Forests, only segments with a <True/> predicate are supported.

   :Parameters:

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

       **n_jobs** : int or None, optional (default=None)
           The number of jobs to run in parallel for the `predict` method.
           ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
           ``-1`` means using all processors.









   .. rubric:: Notes

   Specification: http://dmg.org/pmml/v4-3/MultipleModels.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:: PMMLForestRegressor(pmml, n_jobs=None)


   Bases: :py:obj:`sklearn_pmml_model.base.IntegerEncodingMixin`, :py:obj:`sklearn_pmml_model.base.PMMLBaseRegressor`, :py:obj:`sklearn.ensemble.RandomForestRegressor`

   
   A random forest regressor.

   A random forest is a meta estimator that fits a number of decision tree
   classifiers on various sub-samples of the dataset and uses averaging to
   improve the predictive accuracy and control over-fitting.

   The PMML model consists out of a <Segmentation> element, that contains
   various <Segment> elements. Each segment contains it's own <TreeModel>.
   For Random Forests, only segments with a <True/> predicate are supported.

   :Parameters:

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

       **n_jobs** : int or None, optional (default=None)
           The number of jobs to run in parallel for the `predict` method.
           ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
           ``-1`` means using all processors.









   .. rubric:: Notes

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





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

      
      Not supported: PMML models are already fitted.
















      ..
          !! processed by numpydoc !!

   .. py:method:: _more_tags()



.. py:class:: PMMLGradientBoostingClassifier(pmml)


   Bases: :py:obj:`sklearn_pmml_model.base.IntegerEncodingMixin`, :py:obj:`sklearn_pmml_model.base.PMMLBaseClassifier`, :py:obj:`sklearn.ensemble.GradientBoostingClassifier`, :py:obj:`abc.ABC`

   
   Gradient Boosting for classification.

   GB builds an additive model in a  forward stage-wise fashion; it allows
   for the optimization of arbitrary differentiable loss functions. In each
   stage ``n_classes_`` regression trees are fit on the negative gradient of
   the binomial or multinomial deviance loss function. Binary classification
   is a special case where only a single regression tree is induced.

   The PMML model consists out of a <Segmentation> element, that contains
   various <Segment> elements. Each segment contains it's own <TreeModel>.
   For Gradient Boosting, only segments with a <True/> predicate are supported.

   :Parameters:

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









   .. rubric:: Notes

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





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

      
      Not supported: PMML models are already fitted.
















      ..
          !! processed by numpydoc !!

   .. py:method:: _raw_predict(x)

      
      Override to support categorical features.
















      ..
          !! processed by numpydoc !!

   .. py:method:: _more_tags()



.. py:class:: PMMLGradientBoostingRegressor(pmml)


   Bases: :py:obj:`sklearn_pmml_model.base.IntegerEncodingMixin`, :py:obj:`sklearn_pmml_model.base.PMMLBaseRegressor`, :py:obj:`sklearn.ensemble.GradientBoostingRegressor`, :py:obj:`abc.ABC`

   
   Gradient Boosting for regression.

   GB builds an additive model in a  forward stage-wise fashion; it allows
   for the optimization of arbitrary differentiable loss functions. In each
   stage ``n_classes_`` regression trees are fit on the negative gradient of
   the binomial or multinomial deviance loss function. Binary classification
   is a special case where only a single regression tree is induced.

   The PMML model consists out of a <Segmentation> element, that contains
   various <Segment> elements. Each segment contains it's own <TreeModel>.
   For Gradient Boosting, only segments with a <True/> predicate are supported.

   :Parameters:

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









   .. rubric:: Notes

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





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

      
      Not supported: PMML models are already fitted.
















      ..
          !! processed by numpydoc !!

   .. py:method:: _raw_predict(x)

      
      Override to support categorical features.
















      ..
          !! processed by numpydoc !!

   .. py:method:: _more_tags()