Packages

c

io.citrine.lolo

MultiResult

case class MultiResult[T](values: Seq[T]) extends PredictionResult[T] with Product with Serializable

Linear Supertypes
Serializable, Serializable, Product, Equals, PredictionResult[T], AnyRef, Any
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Inherited
  1. MultiResult
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. PredictionResult
  7. AnyRef
  8. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new MultiResult(values: Seq[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. def append(other: MultiResult.this.type): MultiResult[T]
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  8. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  10. def getExpected(): Seq[T]

    Get the expected values for this prediction

    Get the expected values for this prediction

    returns

    expected value of each prediction

    Definition Classes
    MultiResult → PredictionResult
  11. def getGradient(): Option[Seq[Vector[Double]]]

    Get the gradient or sensitivity of each prediction

    Get the gradient or sensitivity of each prediction

    returns

    a vector of doubles for each prediction

    Definition Classes
    PredictionResult
  12. def getImportanceScores(): Option[Seq[Seq[Double]]]

    Get the training row scores for each prediction

    Get the training row scores for each prediction

    returns

    training row scores of each prediction

    Definition Classes
    PredictionResult
  13. def getInfluenceScores(actuals: Seq[Any]): Option[Seq[Seq[Double]]]

    Get the improvement (positive) or damage (negative) due to each training row on a prediction

    Get the improvement (positive) or damage (negative) due to each training row on a prediction

    actuals

    to assess the improvement or damage against

    returns

    Sequence (over predictions) of sequence (over training rows) of influence

    Definition Classes
    PredictionResult
  14. def getUncertainty(): Option[Seq[Any]]

    Get the uncertainty of the prediction

    Get the uncertainty of the prediction

    For example, in regression this is sqrt(bias^2 + variance)

    returns

    uncertainty of each prediction

    Definition Classes
    PredictionResult
  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
    Annotations
    @native()
  18. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  19. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  20. val values: Seq[T]
  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
    @native() @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from PredictionResult[T]

Inherited from AnyRef

Inherited from Any

Ungrouped