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

   AnyRef

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 binaryProbabilityToPrediction(probability: Double): Double

   Definition Classes

   BinaryClassificationModel

9. def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

10. val coefficients: Vector

    coefficients vector for model

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

    Definition Classes

    AnyRef

12. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

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

    Definition Classes

    AnyRef → Any

14. val intercept: Double

    intercept of model

15. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

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

    Definition Classes

    AnyRef

17. final def notify(): Unit

    Definition Classes

    AnyRef

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

    BinaryClassificationModel → MultinomialClassificationModel

20. 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

21. 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

    LogisticRegressionModel → BinaryClassificationModel

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

    Predict class and probability.

    Predict class and probability.

    features

    features to predict

    returns

    (prediction, probability)

    Definition Classes

    BinaryClassificationModel

23. def predictProbabilities(features: Vector): Vector

    Definition Classes

    MultinomialClassificationModel

24. def predictRaw(features: Vector): Vector

    Definition Classes

    LogisticRegressionModel → MultinomialClassificationModel

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

    Definition Classes

    MultinomialClassificationModel

26. def probabilityToPrediction(probability: Vector): Double

    Definition Classes

    MultinomialClassificationModel

27. def rawToPrediction(raw: Vector): Double

    Definition Classes

    MultinomialClassificationModel

28. def rawToProbability(raw: Vector): Vector

    Definition Classes

    MultinomialClassificationModel

29. def rawToProbabilityInPlace(raw: Vector): Vector

    Definition Classes

    LogisticRegressionModel → MultinomialClassificationModel

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

    Definition Classes

    AnyRef

31. val threshold: Option[Double]

    threshold for pegging predictions

    threshold for pegging predictions

    Definition Classes

    LogisticRegressionModel → BinaryClassificationModel

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

    Definition Classes

    BinaryClassificationModel → MultinomialClassificationModel

33. final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )