Class

ml.combust.mleap.core.classification

ProbabilisticLogisticsRegressionModel

Related Doc: package classification

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case class ProbabilisticLogisticsRegressionModel(coefficientMatrix: Matrix, interceptVector: Vector, thresholds: Option[Array[Double]]) extends AbstractLogisticRegressionModel with Product with Serializable

Linear Supertypes
Serializable, Serializable, Product, Equals, AbstractLogisticRegressionModel, ProbabilisticClassificationModel, ClassificationModel, Model, AnyRef, Any
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Inherited
  1. ProbabilisticLogisticsRegressionModel
  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 ProbabilisticLogisticsRegressionModel(coefficientMatrix: Matrix, interceptVector: Vector, thresholds: Option[Array[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. lazy val binaryModel: BinaryLogisticRegressionModel

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  7. def clone(): AnyRef

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

  8. val coefficientMatrix: Matrix

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

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    Definition Classes
    AnyRef

  10. def finalize(): Unit

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

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

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    Definition Classes
    AnyRef → Any

  12. def inputSchema: StructType

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    Definition Classes
    ProbabilisticClassificationModel → Model

  13. val interceptVector: Vector

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  14. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  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
    ProbabilisticLogisticsRegressionModel → ProbabilisticClassificationModel

  19. val numFeatures: Int

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    Definition Classes
    ProbabilisticLogisticsRegressionModel → 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
    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
    ProbabilisticLogisticsRegressionModel → 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
    ProbabilisticLogisticsRegressionModel → ProbabilisticClassificationModel

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

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    Definition Classes
    AnyRef

  31. val thresholds: Option[Array[Double]]

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    Definition Classes
    ProbabilisticLogisticsRegressionModel → ProbabilisticClassificationModel

  32. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  34. 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

Ungrouped