Hi Alex,
Here is the ticket for refining tree predictions. Let's discuss this
further on the JIRA.
https://issues.apache.org/jira/browse/SPARK-4240
There is no ticket yet for quantile regression. It will be great if you
could create one and note down the corresponding loss function and gradient
c
Manish,
My use case for (asymmetric) absolute error is quite trivially quantile
regression. In other words, I want to use Spark to learn conditional
cumulative distribution functions. See R's GBM quantile regression option.
If you either find or create a Jira ticket, I would be happy to give it a
Hi Alessandro,
I think absolute error as splitting criterion might be feasible with the
current architecture -- I think the sufficient statistics we collect
currently might be able to support this. Could you let us know scenarios
where absolute error has significantly outperformed squared error fo
Manish,
Thanks for pointing me to the relevant docs. It is unfortunate that
absolute error is not supported yet. I can't seem to find a Jira for it.
Now, here's the what the comments say in the current master branch:
/**
* :: Experimental ::
* A class that implements Stochastic Gradient Boostin
Hi Alessandro,
MLlib v1.1 supports variance for regression and gini impurity and entropy
for classification.
http://spark.apache.org/docs/latest/mllib-decision-tree.html
If the information gain calculation can be performed by distributed
aggregation then it might be possible to plug it into the e
