[R] Question on class 1, 2 output for RandomForest

[Melanie Vida]

Hi All,

I read the R-newsletter Volum 2/3, December 2002 on page 18. I tried the 
example there, too. Then, I used a different data set with random Forest 
from the UCI respository. The results for the "credit" data generated 2 
additional columns, column "1" and a column "2" that the example given 
in the newsletter did not generate from the  fgl data set.

For the "credit" data, what does the output with the heading "1", " 2" 
imply for ntree=100...500 (below)? Does the "1" imply the actual data, 
"class 1" and a group of synthetic data "2" -> "class 2"? Did my random 
forest automatically default to unsupervised learning  and automatically 
create the class 2, synthetic data, then classify the combined data with 
the random Forest? If so, which method did R used to generate the 
synthetic data? The newsletter states that there are 2 ways to generate 
synthetic data.

Further, the  parameters to tune these randomForest would ideally 
optimize the OOB error rate and whatever column 1 and 2 error rates 
mean? I tried mtry=2, 3 and 10, but that didn't change the errors much. 
Are these results reasonable, or should I tried to tune different 
parameters for this special case?

ntree      OOB      1      2
  100:  20.72% 14.10% 28.99%
  200:  18.99% 13.58% 25.73%
  300:  19.71% 15.14% 25.41%
  400:  20.00% 14.10% 27.36%
  500:  19.13% 13.58% 26.06%

Call:
 randomForest(x = V16 ~ ., data = credit, mtry = 3, importance = 
TRUE,      do.trace = 100)
               Type of random forest: classification
                     Number of trees: 500
No. of variables tried at each split: 3

        OOB estimate of  error rate: 19.86%
Confusion matrix:
    -   + class.error
- 326  57   0.1488251
+  80 227   0.2605863


Thanks in advance,

-Melanie
-------
# Read in the credit table
credit = 
read.table(url('ftp://ftp.ics.uci.edu/pub/machine-learning-databases/credit-screening/crx.data'),sep=",")
str(credit)
credit$V2 = as.numeric(credit$V2)
credit$V14 = as.numeric(credit$V14)
str(credit)

credit.rf <- randomForest(V16 ~ ., data=credit, mtry=3, importance = 
TRUE, do.trace=100)
print(credit.rf)


-Melanie