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From Kevin Mellott <kevin.r.mell...@gmail.com>
Subject Re: [MLlib] How to set Loss to Gradient Boosted Tree in Java
Date Mon, 29 Feb 2016 15:38:31 GMT
I believe that you can instantiate an instance of the AbsoluteError class
for the *Loss* object, since that object implements the Loss interface. For
example.

val loss = new AbsoluteError()
boostingStrategy.setLoss(loss)

On Mon, Feb 29, 2016 at 9:33 AM, diplomatic Guru <diplomaticguru@gmail.com>
wrote:

> Hi Kevin,
>
> Yes, I've set the bootingStrategy like that using the example. But I'm not
> sure how to create and pass the Loss object.
>
> e.g
>
> boostingStrategy.setLoss(......);
>
> Not sure how to pass the selected Loss.
>
> How do I set the  Absolute Error in setLoss() function?
>
>
>
>
> On 29 February 2016 at 15:26, Kevin Mellott <kevin.r.mellott@gmail.com>
> wrote:
>
>> You can use the constructor that accepts a BoostingStrategy object, which
>> will allow you to set the tree strategy (and other hyperparameters as well).
>>
>> *GradientBoostedTrees
>> <http://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/tree/GradientBoostedTrees.html#GradientBoostedTrees(org.apache.spark.mllib.tree.configuration.BoostingStrategy)>*
>> (BoostingStrategy
>> <http://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/tree/configuration/BoostingStrategy.html>
>>  boostingStrategy)
>>
>> On Mon, Feb 29, 2016 at 9:21 AM, diplomatic Guru <
>> diplomaticguru@gmail.com> wrote:
>>
>>> Hello guys,
>>>
>>> I think the default Loss algorithm is Squared Error for regression, but
>>> how do I change that to Absolute Error in Java.
>>>
>>> Could you please show me an example?
>>>
>>>
>>>
>>
>

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