Packages

c

io.citrine.lolo.trees.regression

RegressionTrainingLeaf

class RegressionTrainingLeaf extends TrainingNode[AnyVal, Double]

Training leaf node for regression trees Created by maxhutch on 3/8/17.

Linear Supertypes
TrainingNode[AnyVal, Double], Serializable, AnyRef, Any
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Inherited
  1. RegressionTrainingLeaf
  2. TrainingNode
  3. Serializable
  4. AnyRef
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Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new RegressionTrainingLeaf(trainingData: Seq[(Vector[AnyVal], Double, Double)], leafLearner: Learner, depth: Int)

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  8. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  9. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  10. def getFeatureImportance(): ArraySeq[Double]

    Pull the leaf model's feature importance and rescale it by the remaining impurity

    Pull the leaf model's feature importance and rescale it by the remaining impurity

    returns

    feature importance as a vector

    Definition Classes
    RegressionTrainingLeaf → TrainingNode
  11. def getNode(): ModelNode[PredictionResult[Double]]

    Wrap the leaf model (previously trained) in a lightweight leaf node

    Wrap the leaf model (previously trained) in a lightweight leaf node

    returns

    lightweight prediction node

    Definition Classes
    RegressionTrainingLeaf → TrainingNode
  12. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  13. val importance: Option[Vector[Double]]

    Pull out the importance for future use

  14. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  15. val leafTrainingResult: TrainingResult

    Train the leaf learner on the training data

  16. val model: Model[PredictionResult[Double]]

    Pull out the model for future use

  17. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  18. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  19. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  20. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  21. def toString(): String
    Definition Classes
    AnyRef → Any
  22. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  23. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  24. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()

Inherited from TrainingNode[AnyVal, Double]

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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