Fiona Callaghan wrote:
> Hi Chris. Thanks for replying. I want to take the pruned list of
> subtrees, and select the final tree from this list using a bootstrap
> technique (rather than cross validation). I think, and I could be wrong,
The result will be very poor predictive accuracy on new samples, because
you are not incorporating shrinkage (penalization) in deriving the
estimates.
Frank
> that bagging takes a bunch of bootstrap samples, grows one tree per
> boostrap sample and then combines the estimates, but this is not want I
> want to do. I hope this is clearer. It is difficult to describe these
> things with eamil.
> Cheers
> Fiona
>> Fiona Callaghan wrote:
>>> I was wondering if someone could help me with an rpart problem. I can
>>> see
>>> that cross-validation is the default for tree selection in rpart -- has
>>> a
>>> bootstrap method been implemented anywhere? I think this is a different
>>> thing to 'bagging' or 'boosting' -- I still want 'one' tree at the end,
>>> I
>>> just would like it chosen using a bootstrap method. Any ideas???
>> Hi Fiona,
>>
>> I'm not sure if I understand you correctly.
>> To get one single rpart tree trained on one bootstrap sample, try
>> bagging() from the 'ipred' package and set nbagg=1.
>>
>> Bye,
>> Chris
>>
>>
>
>
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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