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From DB Tsai <dbt...@dbtsai.com>
Subject Re: Spark Implementation of XGBoost
Date Mon, 26 Oct 2015 23:07:10 GMT
Also, does it support categorical feature?

Sincerely,

DB Tsai
----------------------------------------------------------
Web: https://www.dbtsai.com
PGP Key ID: 0xAF08DF8D


On Mon, Oct 26, 2015 at 4:06 PM, DB Tsai <dbtsai@dbtsai.com> 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: 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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