DecisionTree

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. final def asInstanceOf[T0]: T0

   

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5. def atNode[T](nodeIndex: Int, X: List[List[Double]], y: List[T], sampleWeight: List[Double] = Nil): (List[List[Double]], List[T], List[Double])

   

   Returns the data (instances and labels) present at this node

   Returns the data (instances and labels) present at this node

   nodeIndex

   The node index

   X

   List of instances

   y

   List of labels

   sampleWeight

   Optional list of sample weights

   returns

   List of instances, list of labels and list of sample weights, being a subset of the input X, y and sampleWeight

6. def classify(instance: List[Double]): Int

   

   Classifies an instance based on its feature vector

   Classifies an instance based on its feature vector

   instance

   Feature list of an instance

   returns

   Predicted label

7. def clone(): AnyRef

   

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8. final def eq(arg0: AnyRef): Boolean

   

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9. def equals(arg0: Any): Boolean

   

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10. def finalize(): Unit

    

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    protected[java.lang]

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    @throws( classOf[java.lang.Throwable] )

11. final def getClass(): Class[_]

    

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12. def hashCode(): Int

    

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13. def initTree(nNodes: Int, tree: List[DecisionNode]): List[DecisionNode]

    

    Initializes a list of nodes

    Initializes a list of nodes

    nNodes

    number of nodes to initialize

    tree

    start/intermediate tree object

    returns

    List of nodes

14. final def isInstanceOf[T0]: Boolean

    

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15. def nFilledNodes(): Int

    

    Counts the nodes which have been filled by the user

16. val nNodes: Int

    

    Number of nodes in this tree

17. final def ne(arg0: AnyRef): Boolean

    

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18. final def notify(): Unit

    

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19. final def notifyAll(): Unit

    

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20. def predict(instance: List[Double]): Double

    

    Predicits an instance's label based on its feature vector

    Predicits an instance's label based on its feature vector

    instance

    Feature list of an instance

    returns

    Predicted label

21. def setNode(nodeIndex: Int, featureIndex: Int, threshold: Double, greater: Boolean, mean: Double, nSamples: Int, purity: Double = Double.MinValue): Unit

    

    Sets node attributes

    Sets node attributes

    nodeIndex

    The index of the node to be customized

    featureIndex

    The index of the feature the node decides on

    threshold

    The threshold the node's decision will apply

    greater

    Is the signal region greater or less than the threshold?

    purity

    The purity of the split in this node

22. final def synchronized[T0](arg0: ⇒ T0): T0

    

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23. def toString(): String

    

    Definition Classes

    DecisionTree → AnyRef → Any

24. val tree: List[DecisionNode]

    

    The object holding the nodes

25. def updateNode(nodeIndex: Int, featureIndex: Int, threshold: Double, greater: Boolean, mean: Double, nSamples: Int, purity: Double): Unit

    

    Updates an existing node with new decision instructions, in case its purity is improved

    Updates an existing node with new decision instructions, in case its purity is improved

    nodeIndex

    The index of the node to be customized

    featureIndex

    The index of the feature the decision is based on

    threshold

    The threshold of the proposed decision

    greater

    Is the signal region greater or less than the threshold?

    purity

    The purity of the proposed split

26. final def wait(): Unit

    

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27. final def wait(arg0: Long, arg1: Int): Unit

    

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28. final def wait(arg0: Long): Unit

    

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29. var weight: Double

    

    Weight of the tree (for boosting)