GBTClassifierModel

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. def apply(features: Vector): Double

   

   Alias for ml.combust.mleap.core.classification.ClassificationModel#predict.

   Alias for ml.combust.mleap.core.classification.ClassificationModel#predict.

   features

   feature vector

   returns

   prediction

   Definition Classes

   ClassificationModel

5. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

6. def binaryProbabilityToPrediction(probability: Double): Double

   

   Definition Classes

   BinaryClassificationModel

7. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

   Definition Classes

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   @throws( ... )

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

   

   Definition Classes

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9. def finalize(): Unit

   

   Attributes

   protected[java.lang]

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

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

    

    Definition Classes

    AnyRef → Any

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

14. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

15. val numClasses: Int

    

    Number of classes this model predicts.

    Number of classes this model predicts.

    2 indicates this is a binary classification model. Greater than 2 indicates a multinomial classifier.

    Definition Classes

    BinaryClassificationModel → MultinomialClassificationModel

16. val numFeatures: Int

    

    number of features

17. def numTrees: Int

    

    Number of trees in the ensemble

    Number of trees in the ensemble

    Definition Classes

    TreeEnsemble

18. def predict(features: Vector): Double

    

    Predict the class taking into account threshold.

    Predict the class taking into account threshold.

    features

    features for prediction

    returns

    prediction with threshold

    Definition Classes

    BinaryClassificationModel → MultinomialClassificationModel → ClassificationModel

19. def predictBinaryProbability(features: Vector): Double

    

    Predict the class without taking into account threshold.

    Predict the class without taking into account threshold.

    features

    features for prediction

    returns

    probability that prediction is the predictable class

    Definition Classes

    GBTClassifierModel → BinaryClassificationModel

20. def predictBinaryWithProbability(features: Vector): (Double, Double)

    

    Predict class and probability.

    Predict class and probability.

    features

    features to predict

    returns

    (prediction, probability)

    Definition Classes

    BinaryClassificationModel

21. def predictProbabilities(features: Vector): Vector

    

    Definition Classes

    MultinomialClassificationModel

22. def predictRaw(features: Vector): Vector

    

    Definition Classes

    GBTClassifierModel → MultinomialClassificationModel

23. def predictWithProbability(features: Vector): (Double, Double)

    

    Definition Classes

    MultinomialClassificationModel

24. def probabilityToPrediction(probability: Vector): Double

    

    Definition Classes

    MultinomialClassificationModel

25. def rawToPrediction(raw: Vector): Double

    

    Definition Classes

    MultinomialClassificationModel

26. def rawToProbability(raw: Vector): Vector

    

    Definition Classes

    MultinomialClassificationModel

27. def rawToProbabilityInPlace(raw: Vector): Vector

    

    Definition Classes

    GBTClassifierModel → MultinomialClassificationModel

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

    

    Definition Classes

    AnyRef

29. val threshold: Option[Double]

    

    Threshold for binary classifiers.

    Threshold for binary classifiers.

    If the prediction probability is over this value, then the prediction is pegged to 1.0. Otherwise the prediction is pegged to 0.0.

    Definition Classes

    GBTClassifierModel → BinaryClassificationModel

30. lazy val thresholds: Option[Array[Double]]

    

    Definition Classes

    BinaryClassificationModel → MultinomialClassificationModel

31. val treeWeights: Seq[Double]

    

    weights of each tree

    weights of each tree

    Definition Classes

    GBTClassifierModel → TreeEnsemble

32. val trees: Seq[DecisionTreeRegressionModel]

    

    trees in the gradient boost model

    trees in the gradient boost model

    Definition Classes

    GBTClassifierModel → TreeEnsemble

33. final def wait(): Unit

    

    Definition Classes

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    @throws( ... )

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

    

    Definition Classes

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    @throws( ... )

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

    

    Definition Classes

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    @throws( ... )