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From Meihua Wu <rotationsymmetr...@gmail.com>
Subject Re: Spark Implementation of XGBoost
Date Tue, 27 Oct 2015 03:46:29 GMT
Hi YiZhi,

Thank you for mentioning the jira. I will add a note to the jira.

Meihua

On Mon, Oct 26, 2015 at 6:16 PM, YiZhi Liu <javelinjs@gmail.com> wrote:
> 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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>>
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>
>
>
> --
> Yizhi Liu
> Senior Software Engineer / Data Mining
> www.mvad.com, Shanghai, China

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