Trait

org.apache.spark.mllib.tree.loss

Loss

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trait Loss extends Serializable

:: DeveloperApi :: Trait for adding "pluggable" loss functions for the gradient boosting algorithm.

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@Since( "1.2.0" ) @DeveloperApi()
Source
Loss.scala
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  1. abstract def gradient(prediction: Double, label: Double): Double

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    Method to calculate the gradients for the gradient boosting calculation.

    Method to calculate the gradients for the gradient boosting calculation.

    prediction

    Predicted feature

    label

    true label.

    returns

    Loss gradient.

    Annotations
    @Since( "1.2.0" )

Concrete Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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  6. def computeError(model: TreeEnsembleModel, data: RDD[LabeledPoint]): Double

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    Method to calculate error of the base learner for the gradient boosting calculation.

    Method to calculate error of the base learner for the gradient boosting calculation. Note: This method is not used by the gradient boosting algorithm but is useful for debugging purposes.

    model

    Model of the weak learner.

    data

    Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint.

    returns

    Measure of model error on data

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    @Since( "1.2.0" )
  7. final def eq(arg0: AnyRef): Boolean

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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  13. final def ne(arg0: AnyRef): Boolean

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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  16. final def synchronized[T0](arg0: ⇒ T0): T0

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  19. final def wait(arg0: Long, arg1: Int): Unit

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