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From Meihua Wu <>
Subject Re: Spark Implementation of XGBoost
Date Tue, 27 Oct 2015 03:37:17 GMT
Hi DB Tsai,

Thank you very much for your interest and comment.

1) feature sub-sample is per-node, like random forest.

2) The current code heavily exploits the tree structure to speed up
the learning (such as processing multiple learning node in one pass of
the training data). So a generic GBM is likely to be a different
codebase. Do you have any nice reference of efficient GBM? I am more
than happy to look into that.

3) The algorithm accept training data as a DataFrame with the
featureCol indexed by VectorIndexer. You can specify which variable is
categorical in the VectorIndexer. Please note that currently all
categorical variables are treated as ordered. If you want some
categorical variables as unordered, you can pass the data through
OneHotEncoder before the VectorIndexer. I do have a plan to handle
unordered categorical variable using the approach in RF in Spark ML
(Please see roadmap in the



On Mon, Oct 26, 2015 at 4:06 PM, DB Tsai <> wrote:
> Interesting. For feature sub-sampling, is it per-node or per-tree? Do
> you think you can implement generic GBM and have it merged as part of
> Spark codebase?
> Sincerely,
> DB Tsai
> ----------------------------------------------------------
> Web:
> PGP Key ID: 0xAF08DF8D
> On Mon, Oct 26, 2015 at 11:42 AM, Meihua Wu
> <> wrote:
>> Hi Spark User/Dev,
>> Inspired by the success of XGBoost, I have created a Spark package for
>> gradient boosting tree with 2nd order approximation of arbitrary
>> user-defined loss functions.
>> Currently linear (normal) regression, binary classification, Poisson
>> regression are supported. You can extend with other loss function as
>> well.
>> L1, L2, bagging, feature sub-sampling are also employed to avoid overfitting.
>> Thank you for testing. I am looking forward to your comments and
>> suggestions. Bugs or improvements can be reported through GitHub.
>> Many thanks!
>> Meihua
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