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c

io.citrine.lolo.trees.multitask

MultiTaskTrainingNode

class MultiTaskTrainingNode extends AnyRef

Node in a multi-task training tree, which can produce nodes for its model trees. Splits are chosen using a MultiTaskSplitter, which considers the sum impurity decrease across all tasks.

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Instance Constructors

  1. new MultiTaskTrainingNode(inputs: Seq[(Vector[AnyVal], Array[AnyVal], Double)], numFeatures: Int, maxDepth: Int, minInstances: Int, randomizePivotLocation: Boolean = false, rng: Random = Random)

    inputs

    data on which to select splits and form models

    numFeatures

    to randomly select from at each split (negative values indicate that all features should be considered)

    maxDepth

    to grow the tree to

    minInstances

    minimum number of training instances per leaf

    randomizePivotLocation

    whether to generate splits randomly between the data points

    rng

    random number generator, for reproducibility

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  1. final def !=(arg0: Any): Boolean
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  2. final def ##: Int
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  6. val deltaImpurity: Double
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  10. final def getClass(): Class[_ <: AnyRef]
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  11. def getFeatureImportance(index: Int): ArraySeq[Double]
  12. def getNode(index: Int): ModelNode[PredictionResult[Any]]
  13. def hashCode(): Int
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  14. final def isInstanceOf[T0]: Boolean
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  15. val leftChild: Option[MultiTaskTrainingNode]
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  18. final def notifyAll(): Unit
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  19. val rightChild: Option[MultiTaskTrainingNode]
  20. val split: Split
  21. final def synchronized[T0](arg0: => T0): T0
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  23. final def wait(): Unit
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