ml.combust.mleap.core.classification

BinaryLogisticRegressionModel

case class BinaryLogisticRegressionModel(coefficients: Vector, intercept: Double, threshold: Double) extends AbstractLogisticRegressionModel with Product with Serializable

Linear Supertypes
Serializable, Serializable, Product, Equals, AbstractLogisticRegressionModel, ProbabilisticClassificationModel, ClassificationModel, AnyRef, Any
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  1. BinaryLogisticRegressionModel
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. AbstractLogisticRegressionModel
  7. ProbabilisticClassificationModel
  8. ClassificationModel
  9. AnyRef
  10. Any
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Instance Constructors

  1. new BinaryLogisticRegressionModel(coefficients: Vector, intercept: Double, threshold: Double)

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.

    features

    feature vector

    returns

    prediction

    Definition Classes
    ClassificationModel
  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  8. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. val coefficients: Vector

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

    Definition Classes
    AnyRef
  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 intercept: Double

  14. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  15. def margin(features: Vector): Double

  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
    BinaryLogisticRegressionModelProbabilisticClassificationModel
  20. 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
    BinaryLogisticRegressionModelProbabilisticClassificationModelClassificationModel
  21. def predictProbabilities(features: Vector): Vector

  22. def predictRaw(features: Vector): Vector

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

  24. def probabilityToPrediction(probability: Vector): Double

  25. def rawToPrediction(raw: Vector): Double

  26. def rawToProbability(raw: Vector): Vector

  27. def rawToProbabilityInPlace(raw: Vector): Vector

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

    Definition Classes
    AnyRef
  29. val threshold: Double

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

  31. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  32. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  33. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from ClassificationModel

Inherited from AnyRef

Inherited from Any

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