Packages

c

io.citrine.lolo.trees.regression

RegressionTree

class RegressionTree extends Model[RegressionTreeResult]

Container holding a model node, encoders, and the feature influences

Linear Supertypes
Model[RegressionTreeResult], Serializable, Serializable, AnyRef, Any
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Instance Constructors

  1. new RegressionTree(root: ModelNode[PredictionResult[Double]], encoders: Seq[Option[CategoricalEncoder[Any]]])

    root

    of the tree

    encoders

    for categorical variables

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[java.lang]
    Definition Classes
    AnyRef
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    @native() @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  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 hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  11. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  12. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  14. final def notifyAll(): Unit
    Definition Classes
    AnyRef
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    @native()
  15. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  16. def toString(): String
    Definition Classes
    AnyRef → Any
  17. def transform(inputs: Seq[Vector[Any]]): RegressionTreeResult

    Apply the model by calling predict and wrapping the results

    Apply the model by calling predict and wrapping the results

    inputs

    to apply the model to

    returns

    a predictionresult which includes only the expected outputs

    Definition Classes
    RegressionTree → Model
  18. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  19. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
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    @throws( ... )
  20. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
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    @native() @throws( ... )

Inherited from Model[RegressionTreeResult]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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