Hi all, I have been playing a bit with GradientBoostingClassifier and AdaBoostClassifier and ExtraTrees and while extra trees and adaboost are reasonably fast to fit with there default params (n_estimators=10) on a non toy dataset such as the olivetti faces dataset, the GradientBoostingClassifier was taking ages (I killed it).
The current default value is n_estimators=100 for GradientBoostingClassifier. Maybe it should be aligned to n_estimators=10 as in the other ensemble methods of the scikit? Or was I doing something very stupid by naively running it with the default params on a dataset with size n_samples=400, n_features=4096 and n_classes=40 without any kind of preprocessing? Another way to rephrase that question: what is the typical sweet spot for the dataset shape when doing classification Gradient Boosted Trees? What are reasonable values for the number of estimators in various application domains? -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ This SF email is sponsosred by: Try Windows Azure free for 90 days Click Here http://p.sf.net/sfu/sfd2d-msazure _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
