I remember if you use distribution=bernoulli, then you don't have to
as.factor(your_response_variable) either.
Weiwei
On 5/30/06, Kuhn, Max <[EMAIL PROTECTED]> wrote:
>
> The family arg appears to be the problem. Either bernoulli or adaboost
> are appropriate for classification problems.
>
> Max
>
> > Perhaps by following the Posting Guide you're likely to get more
> helpful
> > responses. You have not shown an example that others can reproduce,
> not
> > given version information for R or gbm. The output you showed does
> not use
> > type="response", either.
> >
> > Andy
> >
> > _____
> >
> > From: r-help-bounces at stat.math.ethz.ch on behalf of stephenc
> > Sent: Sat 5/27/2006 4:02 PM
> > To: 'R Help'
> > Subject: [R] boosting - second posting [Broadcast]
> >
> >
> >
> > Hi
> >
> > I am using boosting for a classification and prediction problem.
> >
> > For some reason it is giving me an outcome that doesn't fall between 0
>
> > and 1 for the predictions. I have tried type="response" but it made
> no
> > difference.
> >
> > Can anyone see what I am doing wrong?
> >
> > Screen output shown below:
> >
> >
> > > boost.model <- gbm(as.factor(train$simNuance) ~ ., # formula
>
> > + data=train, # dataset
> > + # +1: monotone increase,
> > + # 0: no monotone restrictions
>
> > + distribution="gaussian", # bernoulli, adaboost,
> gaussian,
> > + # poisson, and coxph available
>
> > + n.trees=3000, # number of trees
> > + shrinkage=0.005, # shrinkage or learning rate,
> > + # 0.001 to 0.1 usually work
> > + interaction.depth=3, # 1: additive model, 2:
> two-way
> > interactions, etc.
> > + bag.fraction = 0.5, # subsampling fraction, 0.5 is
>
> > probably best
> > + train.fraction = 0.5, # fraction of data for
> training,
> > + # first train.fraction*N used
> > for training
> > + n.minobsinnode = 10, # minimum total weight needed
> in
> > each node
> > + cv.folds = 5, # do 5-fold cross-validation
> > + keep.data=TRUE, # keep a copy of the dataset
> > with the object
> > + verbose=FALSE) # print out progress
> > >
> > > best.iter = gbm.perf(boost.model,method="cv")
> > > pred = predict.gbm(boost.model, test, best.iter)
> > > summary(pred)
> > Min. 1st Qu. Median Mean 3rd Qu. Max.
> > 0.4772 1.5140 1.6760 1.5100 1.7190 1.9420
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>
> ______________________________________________
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> PLEASE do read the posting guide!
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>
--
Weiwei Shi, Ph.D
"Did you always know?"
"No, I did not. But I believed..."
---Matrix III
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