Following on from the previous post, I thought (from reading only and accepting no prior experience with AdaBoost) that the main goal of AdaBoost was to combine weak classifiers (e.g. a depth-restricted DecisionTree) rather than building an ensemble of strong classifiers (as in e.g. a RandomForest).
The example on the site: http://scikit-learn.org/dev/auto_examples/ensemble/plot_forest_iris.html uses DecisionTrees with max_depth=None for each of the 4 classifiers. Using a depth restricted classifier (e.g. max_depth=3) for AdaBoost results in the same classification quality in this example. Might the example say more about AdaBoost's ability to use weak classifiers if we used a restricted depth DecisionTree? Ian. -- Ian Ozsvald (A.I. researcher) i...@ianozsvald.com http://IanOzsvald.com http://MorConsulting.com/ http://Annotate.IO http://SocialTiesApp.com/ http://TheScreencastingHandbook.com http://FivePoundApp.com/ http://twitter.com/IanOzsvald http://ShowMeDo.com ------------------------------------------------------------------------------ This SF.net email is sponsored by Windows: Build for Windows Store. http://p.sf.net/sfu/windows-dev2dev _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
