Class

ml.combust.mleap.core.classification

BinaryLogisticRegressionModel

Related Doc: package classification

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

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

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

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Value Members

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

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

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

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    Definition Classes
    AnyRef → Any

  4. def apply(features: Vector): Double

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    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

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    Definition Classes
    Any

  6. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  7. val coefficients: Vector

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

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    Definition Classes
    AnyRef

  9. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

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    Definition Classes
    AnyRef → Any

  11. def inputSchema: StructType

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    Definition Classes
    ProbabilisticClassificationModel → Model

  12. val intercept: Double

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  13. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  14. def margin(features: Vector): Double

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

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    Definition Classes
    AnyRef

  16. final def notify(): Unit

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    Definition Classes
    AnyRef

  17. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  18. val numClasses: Int

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    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
    BinaryLogisticRegressionModel → ProbabilisticClassificationModel

  19. val numFeatures: Int

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    Definition Classes
    BinaryLogisticRegressionModel → ProbabilisticClassificationModel

  20. def outputSchema: StructType

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    Definition Classes
    ProbabilisticClassificationModel → Model

  21. def predict(features: Vector): Double

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    Predict class based on feature vector.

    Predict class based on feature vector.

    features

    feature vector

    returns

    predicted class or probability

    Definition Classes
    BinaryLogisticRegressionModel → ProbabilisticClassificationModel → ClassificationModel

  22. def predictProbabilities(features: Vector): Vector

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    Definition Classes
    ProbabilisticClassificationModel

  23. def predictRaw(features: Vector): Vector

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    Definition Classes
    BinaryLogisticRegressionModel → ProbabilisticClassificationModel

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

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    Definition Classes
    ProbabilisticClassificationModel

  25. def probabilityToPrediction(probability: Vector): Double

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    Definition Classes
    ProbabilisticClassificationModel

  26. def probabilityToPredictionIndex(probability: Vector): Int

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    Definition Classes
    ProbabilisticClassificationModel

  27. def rawToPrediction(raw: Vector): Double

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    Definition Classes
    ProbabilisticClassificationModel

  28. def rawToProbability(raw: Vector): Vector

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    Definition Classes
    ProbabilisticClassificationModel

  29. def rawToProbabilityInPlace(raw: Vector): Vector

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    Definition Classes
    BinaryLogisticRegressionModel → ProbabilisticClassificationModel

  30. def score(features: Vector): Double

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

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    Definition Classes
    AnyRef

  32. val threshold: Double

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

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    Definition Classes
    ProbabilisticClassificationModel

  34. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from AbstractLogisticRegressionModel

Inherited from ProbabilisticClassificationModel

Inherited from ClassificationModel

Inherited from Model

Inherited from AnyRef

Inherited from Any

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