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Classification Stump
====================

A Classification Decision Stump is a model that consists of a one-level decision tree
where the root is connected to terminal nodes (leaves) [Friedman2017]_.
The library only supports stumps with two leaves.
Two methods of split criterion are available: gini and information gain.
See :ref:`dt_classification` for details.

Batch Processing
****************

A classification stump follows the general workflow described in :ref:`classification_usage_model`.

Training
--------

For a description of the input and output, refer to :ref:`classification_usage_model`.

At the training stage, a classification decision stump has the following parameters:

.. tabularcolumns::  |\Y{0.2}|\Y{0.2}|\Y{0.6}|

.. list-table:: Training Parameters for Classification Stump (Batch Processing)
   :widths: 20 20 60
   :header-rows: 1
   :class: longtable

   * - Parameter
     - Default Value
     - Description
   * - ``algorithmFPType``
     - ``float``
     - The floating-point type that the algorithm uses for intermediate computations. Can be ``float`` or ``double``.
   * - ``method``
     - ``defaultDense``
     - Performance-oriented computation method, the only method supported by the algorithm.
   * - ``splitCriterion``
     - ``decision_tree::classification::gini``
     - Split criteria for classification stump. Two split criterion are available:

       - ``decision_tree::classification::gini``
       - ``decision_tree::classification::infoGain``

       See :ref:`dt_classification` chapter for details.

   * - ``varImportance``
     - ``none``
     - .. note:: Variable importance computation is not supported for current version of the library.
   * - ``nClasses``
     - :math:`2`
     - The number of classes.


Prediction
----------

For a description of the input and output, refer to :ref:`classification_usage_model`.

At the prediction stage, a classification stump has the following parameters:

.. tabularcolumns::  |\Y{0.2}|\Y{0.2}|\Y{0.6}|

.. list-table:: Training Parameters for Classification Stump (Batch Processing)
   :widths: 20 20 60
   :header-rows: 1
   :class: longtable

   * - Parameter
     - Default Value
     - Description
   * - ``algorithmFPType``
     - ``float``
     - The floating-point type that the algorithm uses for intermediate computations. Can be ``float`` or ``double``.
   * - ``method``
     - ``defaultDense``
     - Performance-oriented computation method, the only method supported by the algorithm.
   * - ``nClasses``
     - :math:`2`
     - The number of classes.
   * - ``resultsToEvaluate``
     - ``classifier::computeClassLabels``
     - The form of computed result:

       - ``classifier::computeClassLabels`` – the result contains the ``NumericTable``
         of size :math:`n \times 1` with predicted labels

       - ``classifier::computeClassProbabilities`` – the result contains the ``NumericTable``
         of size :math:`n \times \text{nClasses}` with probabilities to belong to each class

Examples
********

.. tabs::

  .. tab:: C++ (CPU)

    Batch Processing:

    - :cpp_example:`stump_cls_gini_dense_batch.cpp <stump/stump_cls_gini_dense_batch.cpp>`
    - :cpp_example:`stump_cls_infogain_dense_batch.cpp <stump/stump_cls_infogain_dense_batch.cpp>`

  .. tab:: Java*

    .. note:: There is no support for Java on GPU.

    Batch Processing:

    - :java_example:`StumpClsGiniDenseBatch.java <stump/StumpClsGiniDenseBatch.java>`
    - :java_example:`StumpClsInfogainDenseBatch.java <stump/StumpClsInfogainDenseBatch.java>`

  .. tab:: Python*

    Batch Processing:

    - :daal4py_example:`stump_classification_batch.py`