Yes, if there are many distinct values, we need binning to compute the AUC curve. Usually, the scores are not evenly distribution, we cannot simply truncate the digits. Estimating the quantiles for binning is necessary, similar to RangePartitioner: https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/Partitioner.scala#L104 . Limiting the number of bins is definitely useful. Do you have time to work on it? -Xiangrui
On Sun, Nov 2, 2014 at 9:34 AM, Sean Owen <so...@cloudera.com> wrote: > 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 > --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
