LogisticRegressionModel

Value Members

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

   Definition Classes

   AnyRef

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

   Definition Classes

   Any

3. final def ##(): Int

   Definition Classes

   AnyRef → Any

4. final def ==(arg0: AnyRef): Boolean

   Definition Classes

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

   Definition Classes

   Any

6. 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

7. final def asInstanceOf[T0]: T0

   Definition Classes

   Any

8. def binaryModel: BinaryLogisticRegressionModel

9. def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

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   @throws( ... )

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

    Definition Classes

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11. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

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

    Definition Classes

    AnyRef → Any

13. val impl: AbstractLogisticRegressionModel

14. def inputSchema: StructType

    Definition Classes

    ProbabilisticClassificationModel → Model

15. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

16. val isMultinomial: Boolean

17. def multinomialModel: ProbabilisticLogisticsRegressionModel

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

    Definition Classes

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19. final def notify(): Unit

    Definition Classes

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20. final def notifyAll(): Unit

    Definition Classes

    AnyRef

21. 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

22. val numFeatures: Int

    Definition Classes

    LogisticRegressionModel → ProbabilisticClassificationModel

23. def outputSchema: StructType

    Definition Classes

    ProbabilisticClassificationModel → Model

24. 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

25. def predictProbabilities(features: Vector): Vector

    Definition Classes

    ProbabilisticClassificationModel

26. def predictRaw(features: Vector): Vector

    Definition Classes

    LogisticRegressionModel → ProbabilisticClassificationModel

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

    Definition Classes

    ProbabilisticClassificationModel

28. def probabilityToPrediction(probability: Vector): Double

    Definition Classes

    ProbabilisticClassificationModel

29. def probabilityToPredictionIndex(probability: Vector): Int

    Definition Classes

    ProbabilisticClassificationModel

30. def rawToPrediction(raw: Vector): Double

    Definition Classes

    ProbabilisticClassificationModel

31. def rawToProbability(raw: Vector): Vector

    Definition Classes

    ProbabilisticClassificationModel

32. def rawToProbabilityInPlace(raw: Vector): Vector

    Definition Classes

    LogisticRegressionModel → ProbabilisticClassificationModel

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

    Definition Classes

    AnyRef

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

    Definition Classes

    ProbabilisticClassificationModel

35. final def wait(): Unit

    Definition Classes

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    Annotations

    @throws( ... )

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

    Definition Classes

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    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )