Right now I'm using the default values, which means decision tree as the
estimator and learning rate 1.0.
I should probably change the learning rate, at the very least, because I'm not
getting good performance.
Does it make sense to use random forest, instead of decision tree?
Thanks,
From: Jason Wolosonovich [mailto:jmwol...@asu.edu]
Sent: Saturday, April 11, 2015 9:13 AM
To: scikit-learn-general@lists.sourceforge.net
Subject: Re: [Scikit-learn-general] adaboost parameters
What is your dataset like? How are you building your individual classifier that
you are ensembling with AdaBoost? A common-use case would be boosted decision
stumps (one-level decision trees).
http://en.wikipedia.org/wiki/Decision_stump
http://lyonesse.stanford.edu/~langley/papers/stump.ml92.pdf
So with decision stumps and/or a very high learning rate, you would, in
general, need more (relatively speaking) estimators. Whether your dataset has
10 features or 100 features (or more...or less) will be important as well as
the depth of each tree (assuming that you're boosting decision trees). Boosting
is an iterative process, so you'd like as many trees as you can get and a
small-ish learning rate in order to get the best results, with the limiting
factor (as always) being your computational and time budgets, respectively.
My 2 cents. :D
-Jason
From: Pagliari, Roberto [mailto:rpagli...@appcomsci.com]
Sent: Friday, April 10, 2015 1:18 PM
To:
scikit-learn-general@lists.sourceforge.net<mailto:scikit-learn-general@lists.sourceforge.net>
Subject: [Scikit-learn-general] adaboost parameters
When using adaboost, what is a range of values of n_estimators and learning
rate that makes sense to optimize over?
Thank you,
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