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

.. py:module:: sklearn_pmml_model.ensemble.forest


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

Classes
~~~~~~~

.. autoapisummary::

   sklearn_pmml_model.ensemble.forest.PMMLForestClassifier
   sklearn_pmml_model.ensemble.forest.PMMLForestRegressor




.. 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





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

      
      Not supported: PMML models are already fitted.
















      ..
          !! processed by numpydoc !!

   .. 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()