All of the memory allocations for predictions use R's allocVector(). See gbm_pred in gbmentry.cpp. That is, R's memory manager is doing all the work. gbm_pred is not allocating memory separate from R. It's just creating R objects within R that can be deleted or garbage collected.
Make sure your gbm object does not have a "Terms" component. I don't think it should if you called gbm.fit() directly. Without a Terms component it will not run model.frame() and shouldn't really take so much memory. If you want to experiment with the latest development version you can find it at https://r-forge.r-project.org/projects/gbm/ Greg ________________________________ From: Markus Loecher [mailto:markus.loec...@gmail.com] Sent: Friday, October 30, 2009 6:17 AM To: r-help@r-project.org Cc: Ridgeway, Greg Subject: possible memory leak in predict.gbm(), package gbm ? Dear gbm users, When running predict.gbm() on a "large" dataset (150,000 rows, 300 columns, 500 trees), I notice that the memory used by R grows beyond reasonable limits. My 14GB of RAM are often not sufficient. I am interpreting this as a memory leak since there should be no reason to expand memory needs once the data are loaded and passed to predict.gbm() ? Running R version 2.9.2 on Linux, gbm package 1.6-3. Thanks, Markus __________________________________________________________________________ This email message is for the sole use of the intended r...{{dropped:9}} ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
