[R] boosting - second posting

[Liaw, Andy]

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