.. _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 ` .. literalinclude:: plot_adaboost_hastie_10_2.py :lines: 21- **Total running time of the example:** 9.30 seconds ( 0 minutes 9.30 seconds)