Fucang, Questions like these that are specific to one package are best addressed directly to the package maintainer(s) first (me in this case), as the discussion is unlikely to be of general interest to the whole list. 1. The contituent classifier in randomForest uses the CART algorithm (suitably modified for randomForest), based on Leo Breiman's Fortran code. I believe the gut of rpart is written in C by Terry Therneau. 2. There's no built-in functionality for randomForest (or most other algorithms, for that matter) to detect "outliers". 3. The predict() function will need to have the entire forest in memory, in addition to the test set data. There's nothing wrong with predicting the test set in pieces. I routinely do predictions on test sets with > 800,000 cases, but in pieces of sizes 10,000-50,000. HTH, Andy
From: Fucang Jia Hi, erveryone, 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
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