Re: Weka meta bagging and boosting classifiers

Hi Michael,

let's see. Sure, in fact we configure meta-classifiers with base 
learners in ML-Plan (and then configure parameters of both base learner 
and meta-learner) if I we are talking about the same thing.

The question was, from what I understood, whether this also works with 
regressors. No since regressors also have the classifier interface in 
WEKA I have no doubt that this syntactically works, and for bagging I 
don't see any reason why it should not work. For AdaBoost, I guess that 
one needs to adapt the weighting mechanism in the boosting algorithm, 
which in the variant of boosting i am aware of, is based on the error 
rate, which you cannot compute in the case of regression.

Bagging with RandomForests sounds funny. I currently cannot imagine a 
theoretical justification for this phenomenon except maybe that you have 
a kind of inner and outer sampling technique, which might increase 
robustness to over-fitting even more.

So yes, ML-Plan supports the meta-classifiers of WEKA and also stacking.

Thank for following up on this,
Felix

On 26.11.21 01:09, Michael Hall wrote:
> @Felix Mohr
> I’m not sure you are aware that Weka classifiers can be wrapped with meta classifiers.
> Bagging and AdaBoostM1 e.g.
> These sort of chain the classifiers or create a hybrid classifier
> My best Weka results are often Bagging RandomForest. Sort of odd since I found out later that  RandomForest is sort of bagging but often true.
> Does MLPlan support that sort of classifier was sort of the question.
>
> Fwiw, I found at one point that you could even chain the meta classifiers…
> http://mikehall.pairserver.com/ensemble.html
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