LogisticRegressionModel

Value Members

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

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

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

   

   Definition Classes

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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 binaryModel: BinaryLogisticRegressionModel

   

7. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

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

   

   Definition Classes

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9. def finalize(): Unit

   

   Attributes

   protected[java.lang]

   Definition Classes

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   Annotations

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

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

    

    Definition Classes

    AnyRef → Any

11. val impl: AbstractLogisticRegressionModel

    

12. def inputSchema: StructType

    

    Definition Classes

    ProbabilisticClassificationModel → Model

13. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

14. val isMultinomial: Boolean

    

15. def multinomialModel: ProbabilisticLogisticsRegressionModel

    

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

    

    Definition Classes

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17. final def notify(): Unit

    

    Definition Classes

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18. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

19. 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

    LogisticRegressionModel → ProbabilisticClassificationModel

20. val numFeatures: Int

    

    Definition Classes

    LogisticRegressionModel → ProbabilisticClassificationModel

21. def outputSchema: StructType

    

    Definition Classes

    ProbabilisticClassificationModel → Model

22. def predict(features: Vector): Double

    

    Predict class based on feature vector.

    Predict class based on feature vector.

    features

    feature vector

    returns

    predicted class or probability

    Definition Classes

    LogisticRegressionModel → ProbabilisticClassificationModel → ClassificationModel

23. def predictProbabilities(features: Vector): Vector

    

    Definition Classes

    ProbabilisticClassificationModel

24. def predictRaw(features: Vector): Vector

    

    Definition Classes

    LogisticRegressionModel → ProbabilisticClassificationModel

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

    

    Definition Classes

    ProbabilisticClassificationModel

26. def probabilityToPrediction(probability: Vector): Double

    

    Definition Classes

    ProbabilisticClassificationModel

27. def probabilityToPredictionIndex(probability: Vector): Int

    

    Definition Classes

    ProbabilisticClassificationModel

28. def rawToPrediction(raw: Vector): Double

    

    Definition Classes

    ProbabilisticClassificationModel

29. def rawToProbability(raw: Vector): Vector

    

    Definition Classes

    ProbabilisticClassificationModel

30. def rawToProbabilityInPlace(raw: Vector): Vector

    

    Definition Classes

    LogisticRegressionModel → ProbabilisticClassificationModel

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

    

    Definition Classes

    AnyRef

32. def thresholds: Option[Array[Double]]

    

    Definition Classes

    ProbabilisticClassificationModel

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

    

    Definition Classes

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