DecisionTreeClassifier

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 clone(): AnyRef

   

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6. var decisionTree: DecisionTree

   

7. def diagnostics(): Map[String, List[(Double, Double)]]

   

   Provides meta-information on the classifier

   Provides meta-information on the classifier

   returns

   Map object of metric names and metric values

   Definition Classes

   Classifier

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

    

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12. def getPurity(featureX: List[Double], yThisNode: List[Int], wThisNode: List[Double], threshold: Double, crit: String): (Double, Boolean)

    

    Calculates the purity for a cut at threshold at this node

    Calculates the purity for a cut at threshold at this node

    featureX

    List of this feature for all instances at this node

    yThisNode

    List of the corresponding labels

    threshold

    The cut-off threshold to be applied

    crit

    Function to measure the quality of a split

    returns

    The purity of this split and a boolean flag indicating if signal region is greater than the threshold

13. def hashCode(): Int

    

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14. final def isInstanceOf[T0]: Boolean

    

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15. val name: String

    

    The name of the classifier

    The name of the classifier

    Definition Classes

    DecisionTreeClassifier → Classifier

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

    

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17. final def notify(): Unit

    

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18. final def notifyAll(): Unit

    

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19. def predict(X: List[List[Double]]): List[Int]

    

    Applies the trained classifier to a dataset

    Applies the trained classifier to a dataset

    X

    List of data instances

    returns

    List of predictions

    Definition Classes

    DecisionTreeClassifier → Classifier

20. def setOptimalCut(X: List[List[Double]], y: List[Int], sampleWeight: List[Double], decTree: DecisionTree, nodeIndex: Int): Unit

    

    Sets optimal feature and cut-off value for this node

    Sets optimal feature and cut-off value for this node

    X

    Feature vectors of instances at this node

    y

    List of labels of instances at this node

    sampleWeight

    Optional sample weights

    decTree

    Decision tree to update

    nodeIndex

    Index of the node to tune

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

    

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22. def toString(): String

    

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23. def train(X: List[List[Double]], y: List[Int], sampleWeight: List[Double] = Nil): Unit

    

    Performs the training of the classifier

    Performs the training of the classifier

    X

    List of training instances

    y

    List of training labels

    Definition Classes

    DecisionTreeClassifier → Classifier

24. final def wait(): Unit

    

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

    

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

    

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