Got it, thanks so much! Greg.
On Mon, Apr 26, 2010 at 11:02 AM, Ridgeway, Greg <gr...@rand.org> wrote: > Y~X1+X2+X3 is the standard R formula syntax. It simply means "Y is > predicted by X1 and X2 and X3". > > Greg > > ------------------------------ > *From:* Changbin Du [mailto:changb...@gmail.com] > *Sent:* Monday, April 26, 2010 10:21 AM > *To:* Ridgeway, Greg > *Cc:* r-help@r-project.org > *Subject:* Re: R.GBM package > > Thanks so much, Greg! > > On the demo(bernoulli), I FOUND the following information: IT is used for > logistic regression. > > > My question is: when I define a decision tree, can I still use the formula > Y~X1+X2+X3, # formula, even though I dont know the detailed > formula of decision tree. > > Thanks! > > > > > > demo(bernoulli) > ---- ~~~~~~~~~ > > Type <Return> to start : > > > # LOGISTIC REGRESSION EXAMPLE > > > > cat("Running logistic regression example.\n") > Running logistic regression example. > > > # create some data > > N <- 1000 > > > X1 <- runif(N) > > > X2 <- runif(N) > > > X3 <- factor(sample(letters[1:4],N,replace=T)) > > > mu <- c(-1,0,1,2)[as.numeric(X3)] > > > p <- 1/(1+exp(-(sin(3*X1) - 4*X2 + mu))) > > > Y <- rbinom(N,1,p) > > > # random weights if you want to experiment with them > > w <- rexp(N) > > > w <- N*w/sum(w) > > > data <- data.frame(Y=Y,X1=X1,X2=X2,X3=X3) > > > # fit initial model > > gbm1 <- gbm(Y~X1+X2+X3, # formula > + data=data, # dataset > + weights=w, > + var.monotone=c(0,0,0), # -1: monotone decrease, +1: > monotone increase, 0: no monotone restrictions > + distribution="bernoulli", > + n.trees=3000, # number of trees > + shrinkage=0.001, # 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 > + cv.folds=5, # do 5-fold cross-validation > + n.minobsinnode = 10) # minimum total weight needed in > each node > > > > > > > > > > > > > > > > On Mon, Apr 26, 2010 at 9:50 AM, Ridgeway, Greg <gr...@rand.org> wrote: > >> GBM implements boosted trees. It works for 0/1 outcomes, count outcomes, >> continuous outcomes and a few others. You do not need to combine rpart and >> gbm. You're best bet is to just load the package and run a demo >> >demo(bernoulli). >> >> ------------------------------ >> *From:* Changbin Du [mailto:changb...@gmail.com] >> *Sent:* Monday, April 26, 2010 9:48 AM >> *To:* r-help@r-project.org >> *Cc:* Ridgeway, Greg >> *Subject:* R.GBM package >> >> HI, Dear Greg, >> >> I AM A NEW to GBM package. Can boosting decision tree be implemented in >> 'gbm' package? Or 'gbm' can only be used for regression? >> >> IF can, DO I need to combine the rpart and gbm command? >> >> Thanks so much! >> >> >> >> -- >> Sincerely, >> Changbin >> -- >> >> >> >> __________________________________________________________________________ >> >> This email message is for the sole use of the intended recipient(s) and >> may contain confidential information. Any unauthorized review, use, >> disclosure or distribution is prohibited. If you are not the intended >> recipient, please contact the sender by reply email and destroy all copies >> of the original message. >> >> > > > -- > Sincerely, > Changbin > -- > > Changbin Du > DOE Joint Genome Institute > Bldg 400 Rm 457 > 2800 Mitchell Dr > Walnut Creet, CA 94598 > Phone: 925-927-2856 > > > __________________________________________________________________________ > > This email message is for the sole use of the intended...{{dropped:24}} ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
