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...tliers? 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

hi, in the last 2 weeks we play a lot with our new server which we but to a our virtual server for the development and collect some very subjective experience. we use kvm and virt-manager, but sometimes i'm not really sure about the whether it's kvm or virt-manager problem so i collect them together. our system: MB: Intel S3000AHV CPU: Intel Core 2 Quad CPU Q6600 @ 2.40GHz RAM: 8GB CentOS