I got the following results when I run radomForest with below commands:
 
qair <- read.table("train10.dat", header = T)
oz.rf <- randomForest(LESION ~ ., data = qair, ntree = 220,  importance = TRUE)
print(oz.rf)

Call:
 randomForest.formula(x = LESION ~ ., data = qair, ntree = 220,      importance = TRUE) 
               Type of random forest: classification
                     Number of trees: 220
No. of variables tried at each split: 2
        OOB estimate of  error rate: 15.86%
Confusion matrix:
       lesion noninf class.error
lesion   3949    525   0.1173447
noninf    894   3580   0.1998212

What did this mean? Is 11.7% the classification error for 'lesion' class, and 19.98% the classification error for 'noninf' class in the training set?

But when I run below command to test the performance of classification in the same training set.

ntrain <- read.table("train10.dat", header = T)
ntrain.pred <- predict(oz.rf, ntrain)
table(observed = ntrain[, "LESION"], predicted = ntrain.pred)

I got the following results. It seemed that the classification rates for 'lesion' and 'noninf' classes are 0. Any suggestion will be very appreciated.


        predicted
observed lesion noninf
  lesion 4474      0  
  noninf    0   4474  


 



		
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