I show much thanks to Andy and Matthew on former questions. I now sample only a small segment of a image can segment the image into several classes by RandomForest successfully. Now I have some confusion on it:
1. What is the internal component classifier in RandomForest? Are they the CART implemented in the rpart package?
2. I use training samples to predict new samples. But in the population, if I sample not the whole components, but several components I am interested, can randomforest not classify the non-similar components in the testing samples, that is to say, label them as outliers?
3. When random forest is used to predict, the testing samples should be no contribution to the classifiers(which should be done). So I think the memory usage should not increase much, but when I use RF to predict a 256*256*141 samples by 1329 samples (3 variables), on a SGI Octane2 with 2Giga RAM, it runs out of memory. Then I have to segment the big dataset into two, one is 256*256*70, and the other is 256*256*71. Why do RF consume so much memory in the prediction? Does it produce other things other than class label?
Thank you very much!
Fucang
______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
