Re: Input size increasing every iteration of gradient boosted trees [1.4]

Since it sounds like this has been encountered 3 times, and I've personally seen it and mostly verified it, I think it's legit enough for a JIRA: SPARK-10433 I am sorry to say I don't know what is going here though. On Thu, Sep 3, 2015 at 1:56 PM, Peter Rudenko wrote:

Re: Input size increasing every iteration of gradient boosted trees [1.4]

Confirm, having the same issue (1.4.1 mllib package). For smaller dataset accuracy degradeted also. Haven’t tested yet in 1.5 with ml package implementation. |val boostingStrategy = BoostingStrategy.defaultParams("Classification") boostingStrategy.setNumIterations(30)

Input size increasing every iteration of gradient boosted trees [1.4]

I am training a boosted trees model on a couple million input samples (with around 300 features) and am noticing that the input size of each stage is increasing each iteration. For each new tree, the first step seems to be building the decision tree metadata, which does a .count() on the input

Re: Input size increasing every iteration of gradient boosted trees [1.4]

Not that I have any answer at this point, but I was discussing this exact same problem with Johannes today. An input size of ~20K records was growing each iteration by ~15M records. I could not see why on a first look. @jkbradley I know it's not much info but does that ring any bells? I think

Re: Input size increasing every iteration of gradient boosted trees [1.4]

Is this an artifact of a recent change? Does this not show up in any of the tests or benchmarks? On Thu, Aug 13, 2015 at 2:33 PM, Sean Owen so...@cloudera.com wrote: Not that I have any answer at this point, but I was discussing this exact same problem with Johannes today. An input size of