In relation to the above, I see now that my PR has resulted in a slightly broken auto-generated doc page along with a missing image: http://scikit-learn.org/dev/auto_examples/ensemble/plot_forest_iris.html#example-ensemble-plot-forest-iris-py # new with problems http://scikit-learn.org/0.13/auto_examples/ensemble/plot_forest_iris.html#example-ensemble-plot-forest-iris-py # old version with image
I've commented on the (closed) issue but I'm not sure on the correct procedure. Please tell me if I should open a new bug instead? I have noted how to fix 3 formatting issue on that page but I don't know how to include a replacement image? https://github.com/scikit-learn/scikit-learn/pull/2146 Cheers, Ian. On 7 July 2013 21:40, Ian Ozsvald <i...@ianozsvald.com> wrote: > PR for the demo to follow in a day or so once the other email's issues > re. weights are figured out. Much obliged for the notes. > i. > > On 7 July 2013 19:20, Olivier Grisel <olivier.gri...@ensta.org> wrote: >> 2013/7/7 Ian Ozsvald <i...@ianozsvald.com>: >>> 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? >> >> +1, PR accepted :) >> >> Boosting is good for ensembling a large number of underfitting models >> and thus correcting their individual bias. >> Bagging and other randomized voting aggregates is good for ensembling >> a large number of overfitting models and thus correcting their >> individual variance. >> >> -- >> Olivier >> http://twitter.com/ogrisel - http://github.com/ogrisel >> >> ------------------------------------------------------------------------------ >> 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 > > > > -- > 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 -- 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 ------------------------------------------------------------------------------ See everything from the browser to the database with AppDynamics Get end-to-end visibility with application monitoring from AppDynamics Isolate bottlenecks and diagnose root cause in seconds. Start your free trial of AppDynamics Pro today! http://pubads.g.doubleclick.net/gampad/clk?id=48808831&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
