It's strange as you are correct the doc does state it. But it's complaining
about the constructor.
When I clicked on the org.apache.spark.mllib.tree.loss.AbsoluteError class,
this is what I see:
@Since("1.2.0")
@DeveloperApi
object AbsoluteError extends Loss {
/**
* Method to calculate the gradients for the gradient boosting
calculation for least
* absolute error calculation.
* The gradient with respect to F(x) is: sign(F(x) - y)
* @param prediction Predicted label.
* @param label True label.
* @return Loss gradient
*/
@Since("1.2.0")
override def gradient(prediction: Double, label: Double): Double = {
if (label - prediction < 0) 1.0 else -1.0
}
override private[mllib] def computeError(prediction: Double, label:
Double): Double = {
val err = label - prediction
math.abs(err)
}
}
On 29 February 2016 at 15:49, Kevin Mellott <kevin.r.mell...@gmail.com>
wrote:
> Looks like it should be present in 1.3 at
> org.apache.spark.mllib.tree.loss.AbsoluteError
>
>
> spark.apache.org/docs/1.3.0/api/java/org/apache/spark/mllib/tree/loss/AbsoluteError.html
>
> On Mon, Feb 29, 2016 at 9:46 AM, diplomatic Guru <diplomaticg...@gmail.com
> > wrote:
>
>> AbsoluteError() constructor is undefined.
>>
>> I'm using Spark 1.3.0, maybe it is not ready for this version?
>>
>>
>>
>> On 29 February 2016 at 15:38, Kevin Mellott <kevin.r.mell...@gmail.com>
>> wrote:
>>
>>> 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 <
>>> diplomaticg...@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.mell...@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 <
>>>>> diplomaticg...@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?
>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
