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.

Annotations
@Since( "1.2.0" ) @DeveloperApi()
Source
Loss.scala
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Serializable, Serializable, AnyRef, Any
Known Subclasses
AbsoluteError, SquaredError
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Abstract Value Members

  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.

    model

    Model of the weak learner.

    data

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

    returns

    Measure of model error on data

    Annotations
    @Since( "1.2.0" )
    Note

    This method is not used by the gradient boosting algorithm but is useful for debugging purposes.

  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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  10. final def getClass(): Class[_]

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  11. def hashCode(): Int

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  12. final def isInstanceOf[T0]: Boolean

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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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  17. def toString(): String

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  18. final def wait(): Unit

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

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  20. final def wait(arg0: Long): Unit

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