ore than
> shrinkage).
>
> Thanks.
>
> Sincerely,
>
> DB Tsai
> --
> Web: https://www.dbtsai.com
> PGP Key ID: 0xAF08DF8D
>
>
> On Mon, Oct 26, 2015 at 8:37 PM, Meihua Wu <rotationsymmetr...@gmail.com>
> wrote:
>> Hi DB
>>>
>>> DB Tsai
>>> ------
>>> Web: https://www.dbtsai.com
>>> PGP Key ID: 0xAF08DF8D
>>>
>>>
>>> On Mon, Oct 26, 2015 at 11:42 AM, Meihua Wu
>>> <rotationsymmetr...@gma
gt; Sincerely,
>
> DB Tsai
> --
> Web: https://www.dbtsai.com
> PGP Key ID: 0xAF08DF8D
>
>
> On Mon, Oct 26, 2015 at 11:42 AM, Meihua Wu
> <rotationsymmetr...@gmail.com> wrote:
>> Hi Spark User/Dev,
>
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,
please add "setFitIntercept(false)" to your LinearRegression.
LinearRegression by default includes an intercept in the model, e.g.
label = intercept + features dot weight
To get the result you want, you need to force the intercept to be zero.
Just curious, are you trying to solve systems of
I think in the SGD algorithm, the mini batch sample is done without
replacement. So with fraction=1, then all the rows will be sampled
exactly once to form the miniBatch, resulting to the
deterministic/classical case.
On Fri, Aug 7, 2015 at 9:05 AM, Feynman Liang fli...@databricks.com wrote:
at 11:16 AM, Meihua Wu rotationsymmetr...@gmail.com
wrote:
I think in the SGD algorithm, the mini batch sample is done without
replacement. So with fraction=1, then all the rows will be sampled
exactly once to form the miniBatch, resulting to the
deterministic/classical case.
On Fri, Aug 7
is relative small to fit)?
On Tue, Aug 4, 2015 at 8:23 AM, Richard Marscher
rmarsc...@localytics.com wrote:
Yes it does, in fact it's probably going to be one of the more expensive
shuffles you could trigger.
On Mon, Aug 3, 2015 at 12:56 PM, Meihua Wu rotationsymmetr...@gmail.com
wrote:
Does
Does RDD.cartesian involve shuffling?
Thanks!
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