c

io.citrine.lolo.bags

BaggedTrainingResult

class BaggedTrainingResult[+T] extends TrainingResult

Linear Supertypes
TrainingResult, Serializable, AnyRef, Any
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Inherited
  1. BaggedTrainingResult
  2. TrainingResult
  3. Serializable
  4. AnyRef
  5. Any
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Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new BaggedTrainingResult(models: ParSeq[Model[PredictionResult[T]]], featureImportance: Option[Vector[Double]], Nib: Vector[Vector[Int]], trainingData: Seq[(Vector[Any], Any)], useJackknife: Boolean, biasModel: Option[Model[PredictionResult[T]]] = None, rescale: Double = 1.0, disableBootstrap: Boolean = false)(implicit arg0: ClassTag[T])

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. lazy val NibT: Vector[Vector[Int]]
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  8. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  9. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  10. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  11. def getFeatureImportance(): Option[Vector[Double]]

    Average the influences across the ensemble of models

    Average the influences across the ensemble of models

    returns

    feature influences as an array of doubles

    Definition Classes
    BaggedTrainingResult → TrainingResult
  12. def getLoss(): Option[Double]

    Get a measure of the loss of the model, e.g.

    Get a measure of the loss of the model, e.g. RMS OOB error

    Definition Classes
    BaggedTrainingResult → TrainingResult
  13. def getModel(): BaggedModel[Any]

    Get the model contained in the training result

    Get the model contained in the training result

    returns

    the model

    Definition Classes
    BaggedTrainingResult → TrainingResult
  14. def getPredictedVsActual(): Option[Seq[(Vector[Any], Any, Any)]]

    Get the predicted vs actual values, e.g.

    Get the predicted vs actual values, e.g. from OOB

    returns

    seq of (feature vector, predicted value, and actual value)

    Definition Classes
    BaggedTrainingResult → TrainingResult
  15. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  16. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  17. lazy val loss: Double
  18. lazy val model: BaggedModel[T]
  19. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  20. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  21. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  22. lazy val predictedVsActual: Seq[(Vector[Any], Any, Any)]
  23. lazy val rep: Any
  24. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  25. def toString(): String
    Definition Classes
    AnyRef → Any
  26. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  27. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  28. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()

Inherited from TrainingResult

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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