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...{{dropped:24}}
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