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From DB Tsai <dbt...@dbtsai.com>
Subject Re: Spark Implementation of XGBoost
Date Mon, 26 Oct 2015 23:06:14 GMT
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: https://www.dbtsai.com
PGP Key ID: 0xAF08DF8D


On Mon, Oct 26, 2015 at 11:42 AM, Meihua Wu
<rotationsymmetry14@gmail.com> 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.
>
> https://github.com/rotationsymmetry/SparkXGBoost
>
> 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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