Object

org.apache.spark.mllib.tree.loss

AbsoluteError

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object AbsoluteError extends Loss

:: DeveloperApi :: Class for absolute error loss calculation (for regression).

The absolute (L1) error is defined as: |y - F(x)| where y is the label and F(x) is the model prediction for features x.

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@Since( "1.2.0" ) @DeveloperApi()
Source
AbsoluteError.scala
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Loss, Serializable, Serializable, AnyRef, Any
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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

    Definition Classes
    Loss
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    @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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  10. final def getClass(): Class[_]

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  11. def gradient(prediction: Double, label: Double): Double

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    Method to calculate the gradients for the gradient boosting calculation for least absolute error calculation.

    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)

    prediction

    Predicted label.

    label

    True label.

    returns

    Loss gradient

    Definition Classes
    AbsoluteError → Loss
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    @Since( "1.2.0" )

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