This might be a question for Xiangrui. Recently I was using BinaryClassificationMetrics to build an AUC curve for a classifier over a reasonably large number of points (~12M). The scores were all probabilities, so tended to be almost entirely unique.
The computation does some operations by key, and this ran out of memory. It's something you can solve with more than the default amount of memory, but in this case, it seemed unuseful to create an AUC curve with such fine-grained resolution. I ended up just binning the scores so there were ~1000 unique values and then it was fine. Does that sound generally useful as some kind of parameter? or am I missing a trick here. Sean --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
