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From YiZhi Liu <javeli...@gmail.com>
Subject Re: Spark Implementation of XGBoost
Date Tue, 27 Oct 2015 01:16:48 GMT
There's an xgboost exploration jira SPARK-8547. Can it be a good start?

2015-10-27 7:07 GMT+08:00 DB Tsai <dbtsai@dbtsai.com>:
> 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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-- 
Yizhi Liu
Senior Software Engineer / Data Mining
www.mvad.com, Shanghai, China

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