sklearn_pmml_model.ensemble.gb
==============================

.. py:module:: sklearn_pmml_model.ensemble.gb


Classes
-------

.. autoapisummary::

   sklearn_pmml_model.ensemble.gb.PMMLGradientBoostingClassifier
   sklearn_pmml_model.ensemble.gb.PMMLGradientBoostingRegressor


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

.. 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:attribute:: template_estimator


   .. py:attribute:: categorical


   .. 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:attribute:: n_outputs_
      :value: 1



   .. py:attribute:: template_estimator


   .. py:attribute:: categorical


   .. 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()