DecisionTreeRegressor

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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   protected[java.lang]

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6. var decisionTree: DecisionTree

   

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

   

   Provides meta-information on the regressor

   Provides meta-information on the regressor

   returns

   Map object of metric names and metric values

   Definition Classes

   Regressor

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], threshold: Double): Double

    

    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

    threshold

    The cut-off threshold to be applied

    returns

    The purity of this split

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 regressor

    The name of the regressor

    Definition Classes

    DecisionTreeRegressor → Regressor

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[Double]

    

    Applies the trained regressor to a dataset

    Applies the trained regressor to a dataset

    X

    List of data instances

    returns

    List of predictions

    Definition Classes

    DecisionTreeRegressor → Regressor

20. def setOptimalCut(X: List[List[Double]], y: 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

    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[Double]): Unit

    

    Performs the training of the regressor

    Performs the training of the regressor

    X

    List of training instances

    y

    List of training labels

    Definition Classes

    DecisionTreeRegressor → Regressor

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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