:py:mod:`sklearn_pmml_model.tree`
=================================

.. py:module:: sklearn_pmml_model.tree

.. autoapi-nested-parse::

   
   The :mod:`sklearn_pmml_model.tree` module includes decision tree-based models for
   classification and regression.
















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

   tree/index.rst


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

Classes
~~~~~~~

.. autoapisummary::

   sklearn_pmml_model.tree.PMMLTreeClassifier
   sklearn_pmml_model.tree.PMMLTreeRegressor



Functions
~~~~~~~~~

.. autoapisummary::

   sklearn_pmml_model.tree.get_tree
   sklearn_pmml_model.tree.clone



.. py:class:: PMMLTreeClassifier(pmml)


   Bases: :py:obj:`sklearn_pmml_model.base.PMMLBaseClassifier`, :py:obj:`sklearn.tree.DecisionTreeClassifier`

   
   A decision tree classifier.

   The PMML model consists out of a <TreeModel> element, containing at least one
   <Node> element. Every node element contains a predicate, and optional <Node>
   children. Leaf nodes either have a score attribute or <ScoreDistribution>
   child describing the classification output.

   :Parameters:

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









   .. rubric:: Notes

   Specification: http://dmg.org/pmml/v4-3/TreeModel.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:: PMMLTreeRegressor(pmml)


   Bases: :py:obj:`sklearn_pmml_model.base.PMMLBaseRegressor`, :py:obj:`sklearn.tree.DecisionTreeRegressor`

   
   A decision tree regressor.

   The PMML model consists out of a <TreeModel> element, containing at least one
   <Node> element. Every node element contains a predicate, and optional <Node>
   children. Leaf nodes either have a score attribute or <ScoreDistribution>
   child describing the classification output.

   :Parameters:

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









   .. rubric:: Notes

   Specification: http://dmg.org/pmml/v4-3/TreeModel.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:function:: get_tree(est, segment, rescale_factor=1) -> object

   
   Construct a single tree for a <Segment> PMML element.


   :Parameters:

       **est:**
           The estimator to built the tree for. Should contain `template_estimator` and
           `field_mapping` attributes.

       **segment** : eTree.Element
           <Segment> element containing the decision tree to be imported.
           Only segments with a <True/> predicate are supported.

       **rescale_factor** : float
           Factor to scale the output of every node with. Required for gradient
           boosting trees. Optional, and 1 by default.

   :Returns:

       **tree** : sklearn.tree.DecisionTreeClassifier, sklearn.tree.DecisionTreeRegressor
           The sklearn decision tree instance imported from the provided segment,
           matching the type specified in est.template_estimator.













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.. py:function:: clone(est, safe=True)

   
   Clone a DecisionTree, including private properties that are ignored in sklearn.base.clone.


   :Parameters:

       **est** : BaseEstimator
           The estimator or group of estimators to be cloned.

       **safe** : boolean, optional
           If safe is false, clone will fall back to a deep copy on objects
           that are not estimators.














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