Re: [R] boosting - second posting
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. MedianMean 3rd Qu.Max.
> > 0.4772 1.5140 1.6760 1.5100 1.7190 1.9420
> --
> LEGAL NOTICE\ Unless expressly stated otherwise, this messag...{{dropped}}
>
> __
> R-help@stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
> http://www.R-project.org/posting-guide.html
>
--
Weiwei Shi, Ph.D
"Did you always know?"
"No, I did not. But I believed..."
---Matrix III
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[R] boosting - second posting
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. MedianMean 3rd Qu.Max.
> 0.4772 1.5140 1.6760 1.5100 1.7190 1.9420
--
LEGAL NOTICE\ Unless expressly stated otherwise, this messag...{{dropped}}
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] boosting - second posting
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: [EMAIL PROTECTED] 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$simNuance) ~ ., # formula + data=train, # dataset + # +1: monotone increase, + # 0: no monotone restrictions + distribution="gaussian", # bernoulli, adaboost, gaussian, + # poisson, and coxph available + n.trees=3000,# number of trees + shrinkage=0.005, # shrinkage or learning rate, + # 0.001 to 0.1 usually work + interaction.depth=3, # 1: additive model, 2: two-way interactions, etc. + bag.fraction = 0.5, # subsampling fraction, 0.5 is probably best + train.fraction = 0.5,# fraction of data for training, + # first train.fraction*N used for training + n.minobsinnode = 10, # minimum total weight needed in each node + cv.folds = 5,# do 5-fold cross-validation + keep.data=TRUE, # keep a copy of the dataset with the object + verbose=FALSE)# print out progress > > best.iter = gbm.perf(boost.model,method="cv") > pred = predict.gbm(boost.model, test, best.iter) > summary(pred) Min. 1st Qu. MedianMean 3rd Qu.Max. 0.4772 1.5140 1.6760 1.5100 1.7190 1.9420 __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] boosting - second posting
Hi I am using boosting for a classification and prediction problem. For some reason it is giving me an outcome that doesn't fall between 0 and 1 for the predictions. I have tried type="response" but it made no difference. Can anyone see what I am doing wrong? Screen output shown below: > boost.model <- gbm(as.factor(train$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. MedianMean 3rd Qu.Max. 0.4772 1.5140 1.6760 1.5100 1.7190 1.9420 Checked by AVG Free Edition. __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
