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.. _regression_usage_model:

Regression Usage Model
======================

A typical workflow for regression methods includes training and prediction, as explained below.

Algorithm-Specific Parameters
*****************************

The parameters used by regression algorithms at each stage depend on a specific algorithm.
For a list of these parameters, refer to the description of an appropriate regression algorithm.

Training Stage
**************

.. figure:: images/training-stage-regression.png
    :width: 600
    :alt:

    Regression Usage Model: Training Stage

At the training stage, regression algorithms accept the input described below.
Pass the ``Input ID`` as a parameter to the methods that provide input for your algorithm.
For more details, see :ref:`algorithms`.

.. tabularcolumns::  |\Y{0.2}|\Y{0.8}|

.. list-table:: Training Input for Regression Algorithms
   :widths: 10 60
   :header-rows: 1
   :class: longtable

   * - Input ID
     - Input
   * - ``data``
     - Pointer to the :math:`n \times p` numeric table with the training data set.
       This table can be an object of any class derived from ``NumericTable``.

   * - ``weights``
     - Weights of the observations in the training data set. Optional argument.
   * - ``dependentVariables``
     - Pointer to the :math:`n \times k` numeric table with responses (:math:`k` dependent variables).
       This table can be an object of any class derived from ``NumericTable``
       except ``PackedSymmetricMatrix`` and ``PackedTriangularMatrix``.

At the training stage, regression algorithms calculate the result described below.
Pass the ``Result ID`` as a parameter to the methods that access the results of your algorithm.
For more details, see :ref:`algorithms`.

.. tabularcolumns::  |\Y{0.2}|\Y{0.8}|

.. list-table:: Training Output for Regression Algorithms
   :widths: 10 60
   :header-rows: 1

   * - Result ID
     - Result
   * - ``model``
     - Pointer to the regression model being trained. The result can only be an object of the ``Model`` class.

Prediction Stage
****************

.. figure:: images/prediction-stage-regression.png
    :width: 600
    :alt:

    Regression Usage Model: Prediction Stage

At the prediction stage, regression algorithms accept the input described below.
Pass the ``Input ID`` as a parameter to the methods that provide input for your algorithm.
For more details, see :ref:`algorithms`.

.. tabularcolumns::  |\Y{0.2}|\Y{0.8}|

.. list-table:: Prediction Input for Regression Algorithms
   :widths: 10 60
   :header-rows: 1
   :class: longtable

   * - Input ID
     - Input
   * - ``data``
     - Pointer to the :math:`n \times p` numeric table with the working data set.
       This table can be an object of any class derived from ``NumericTable``.

   * - ``model``
     - Pointer to the trained regression model. This input can only be an object of the ``Model`` class.

At the prediction stage, regression algorithms calculate the result described below.
Pass the ``Result ID`` as a parameter to the methods that access the results of your algorithm.
For more details, see :ref:`algorithms`.

.. tabularcolumns::  |\Y{0.2}|\Y{0.8}|

.. list-table:: Prediction Output for Regression Algorithms
   :widths: 10 60
   :header-rows: 1

   * - Result ID
     - Result
   * - ``prediction``
     - Pointer to the :math:`n \times k` numeric table with responses (:math:`k` dependent variables).

       By default, this table is an object of the ``HomogenNumericTable`` class,
       but you can define it as an object of any class derived from ``NumericTable``
       except ``PackedSymmetricMatrix`` and ``PackedTriangularMatrix``.