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 > ---------------------------------------------------------------------- > 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 [[alternative HTML version deleted]] ______________________________________________ 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