c

io.citrine.lolo.linear

LinearRegressionResult

class LinearRegressionResult extends PredictionResult[Double]

Simple container around the result and coefficient array

Linear Supertypes
PredictionResult[Double], AnyRef, Any
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  1. LinearRegressionResult
  2. PredictionResult
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Visibility
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Instance Constructors

  1. new LinearRegressionResult(values: Seq[Double], grad: Vector[Double])

    values

    computed from the model

    grad

    gradient vector, which are just the linear coefficients

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
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  5. def clone(): AnyRef
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    protected[java.lang]
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    @native() @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
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  7. def equals(arg0: Any): Boolean
    Definition Classes
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  8. def finalize(): Unit
    Attributes
    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]
    Definition Classes
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    @native()
  10. def getExpected(): Seq[Double]

    Get the expected values for this prediction

    Get the expected values for this prediction

    returns

    expected value of each prediction

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

    Get the gradient, which is uniform

    Get the gradient, which is uniform

    returns

    a vector of doubles for each prediction

    Definition Classes
    LinearRegressionResult → 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. def hashCode(): Int
    Definition Classes
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    @native()
  16. final def isInstanceOf[T0]: Boolean
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  17. final def ne(arg0: AnyRef): Boolean
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  18. final def notify(): Unit
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    @native()
  19. final def notifyAll(): Unit
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  20. final def synchronized[T0](arg0: ⇒ T0): T0
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  21. def toString(): String
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  22. final def wait(): Unit
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  23. final def wait(arg0: Long, arg1: Int): Unit
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  24. final def wait(arg0: Long): Unit
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Inherited from PredictionResult[Double]

Inherited from AnyRef

Inherited from Any

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