.. _example_ensemble_plot_adaboost_hastie_10_2.py:


=============================
Discrete versus Real AdaBoost
=============================

This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates
the difference in performance between the discrete SAMME [2] boosting
algorithm and real SAMME.R boosting algorithm. Both algorithms are evaluated
on a binary classification task where the target Y is a non-linear function
of 10 input features.

Discrete SAMME AdaBoost adapts based on errors in predicted class labels
whereas real SAMME.R uses the predicted class probabilities.

.. [1] T. Hastie, R. Tibshirani and J. Friedman, "Elements of Statistical
    Learning Ed. 2", Springer, 2009.

.. [2] J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class AdaBoost", 2009.




.. image:: images/plot_adaboost_hastie_10_2_001.png
    :align: center




**Python source code:** :download:`plot_adaboost_hastie_10_2.py <plot_adaboost_hastie_10_2.py>`

.. literalinclude:: plot_adaboost_hastie_10_2.py
    :lines: 21-

**Total running time of the example:**  9.10 seconds
( 0 minutes  9.10 seconds)