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AdaBoost Classifier
===================

AdaBoost (short for "Adaptive Boosting") is a popular boosting classification algorithm.
AdaBoost algorithm performs well on a variety of data sets except some noisy data [Freund99]_.

AdaBoost is a binary classifier.
For a multi-class case, use :ref:`svm_multi_class` framework of the library.

Details
*******

Given :math:`n` feature vectors :math:`x_1 = (x_{11}, \ldots, x_{1p}), \ldots, x_n = (x_{n1}, \ldots, x_{np})` of size :math:`p`
and a vector of class labels :math:`y= (y_1, \ldots, y_n)`, where :math:`y_i \in K = \{-1, 1\}`
describes the class to which the feature vector :math:`x_i` belongs, and a weak learner algorithm,
the problem is to build an AdaBoost classifier.

Training Stage
--------------

The following scheme shows the major steps of the algorithm:

#. Initialize weights :math:`D_1(i) = \frac{1}{n}` for :math:`i = 1, \ldots, n`.

#. For :math:`t = 1, \ldots, T`:

   #. Train the weak learner :math:`h_t(t) \in \{-1, 1\}` using weights :math:`D_t.`
   #. Choose a confidence value :math:`\alpha_t`.
   #. Update :math:`D_{t+1}(i) = \frac {D_t(i)\exp(-\alpha_t Y_i h_t(x_i))} {Z_t}`, where :math:`Z_t` is a normalization factor.

#. Output the final hypothesis:

   .. math::
        H(x_i) = \mathrm{sign} \left( \sum _{t=1}^{T} \alpha_t h_t(x_i)\right)

Prediction Stage
----------------

Given the AdaBoost classifier and :math:`r` feature vectors :math:`x_1, \ldots, x_r`, the problem is to calculate the final class:

.. math::
    H(x_i) = \mathrm{sign} \left( \sum _{t=1}^{T} \alpha_t h_t(x_i)\right)

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

AdaBoost classifier 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, an AdaBoost classifier has the following parameters:

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

.. list-table:: Training Parameters for AdaBoost Classifier (Batch Processing)
   :header-rows: 1
   :widths: 10 20 30
   :align: left
   :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``
     - The computation method used by the AdaBoost classifier. The only training method supported so far is the Y. Freund's method.
   * - ``weakLearnerTraining``
     - Pointer to an object of the stump training class
     - Pointer to the training algorithm of the weak learner. By default, a stump weak learner is used.
   * - ``weakLearnerPrediction``
     - Pointer to an object of the stump prediction class
     - Pointer to the prediction algorithm of the weak learner. By default, a stump weak learner is used.
   * - ``accuracyThreshold``
     - :math:`0.01`
     - AdaBoost training accuracy.
   * - ``maxIterations``
     - :math:`100`
     - The maximal number of iterations for the algorithm.


Prediction
----------

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

At the prediction stage, an AdaBoost classifier has the following parameters:

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

.. list-table:: Prediction Parameters for AdaBoost Classifier (Batch Processing)
   :header-rows: 1
   :widths: 10 20 30
   :align: left
   :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 AdaBoost classifier at the prediction stage.
   * - ``weakLearnerPrediction``
     - Pointer to an object of the stump prediction class
     - Pointer to the prediction algorithm of the weak learner. By default, a stump weak learner is used.

Examples
********

.. tabs::

  .. tab:: C++ (CPU)

    Batch Processing:

    - :cpp_example:`adaboost_dense_batch.cpp <boosting/adaboost_dense_batch.cpp>`

  .. tab:: Java*

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

    Batch Processing:

    - :java_example:`AdaBoostDenseBatch.java <boosting/adaboost/AdaBoostDenseBatch.java>`

  .. tab:: Python*

    - :daal4py_example:`adaboost_batch.py`