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Posted to user@spark.apache.org by Daniel Bendavid <da...@creditkarma.com> on 2014/07/14 20:24:18 UTC

Gradient Boosted Machines

Hi,

My company is strongly considering implementing a recommendation engine that is built off of statistical models using Spark.  We attended the Spark Summit and were incredibly impressed with the technology and the entire community.  Since then, we have been exploring the technology and determining how we could use it for our specific needs.

One algorithm that we ideally want to use as part of our project is Gradient Boosted Machines.  We are aware that they have not yet been implemented in MLib and would like to submit our request that they be considered for future implementation.  Additionally, we would love to see the AdaBoost algorithm implemented in Mlib and Feature Preprocessing implemented in Python (as it already exists for Scala).

Otherwise, thank you for taking our feedback and for providing us with this incredible technology.

Daniel

Re: Gradient Boosted Machines

Posted by Manish Amde <ma...@gmail.com>.

Hi Daniel,

Thanks a lot for your interest. Gradient boosting and AdaBoost algorithms
are under active development and should be a part of release 1.2.

-Manish


On Mon, Jul 14, 2014 at 11:24 AM, Daniel Bendavid <
daniel.bendavid@creditkarma.com> wrote:

>  Hi,
>
>  My company is strongly considering implementing a recommendation engine
> that is built off of statistical models using Spark.  We attended the Spark
> Summit and were incredibly impressed with the technology and the entire
> community.  Since then, we have been exploring the technology and
> determining how we could use it for our specific needs.
>
>  One algorithm that we ideally want to use as part of our project is
> Gradient Boosted Machines.  We are aware that they have not yet been
> implemented in MLib and would like to submit our request that they be
> considered for future implementation.  Additionally, we would love to see
> the AdaBoost algorithm implemented in Mlib and Feature Preprocessing
> implemented in Python (as it already exists for Scala).
>
>  Otherwise, thank you for taking our feedback and for providing us with
> this incredible technology.
>
>  Daniel
>