org.apache.spark.mllib.tree.loss

AbsoluteError

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.

Annotations
@DeveloperApi()
Linear Supertypes
Loss, Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. AbsoluteError
  2. Loss
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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

    Method to calculate loss 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

    Ensemble model

    data

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

    returns

    Mean absolute error of model on data

    Definition Classes
    AbsoluteError → Loss

  9. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  10. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  11. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes
    AnyRef → Any

  13. def gradient(model: TreeEnsembleModel, point: LabeledPoint): Double

    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)

    model

    Ensemble model

    point

    Instance of the training dataset

    returns

    Loss gradient

    Definition Classes
    AbsoluteError → Loss

  14. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  15. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

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

    Definition Classes
    AnyRef

  17. final def notify(): Unit

    Definition Classes
    AnyRef

  18. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    AnyRef

  20. def toString(): String

    Definition Classes
    AnyRef → Any

  21. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Loss

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped