ml.combust.mleap.core.classification

OneVsRestModel

case class OneVsRestModel(classifiers: Array[BinaryClassificationModel]) extends Product with Serializable

Class for multinomial one vs rest models.

One vs rest models are comprised of a series of BinaryClassificationModels which are used to predict each class.

classifiers

binary classification models

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Instance Constructors

  1. new OneVsRestModel(classifiers: Array[BinaryClassificationModel])

    classifiers

    binary classification models

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
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  6. def apply(features: Vector): Double

    Alias for ml.combust.mleap.core.classification.OneVsRestModel#predict.

    Alias for ml.combust.mleap.core.classification.OneVsRestModel#predict.

    features

    feature vector

    returns

    prediction

  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  8. val classifiers: Array[BinaryClassificationModel]

    binary classification models

  9. def clone(): AnyRef

    Attributes
    protected[java.lang]
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    @throws( ... )

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

    Definition Classes
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  11. def finalize(): Unit

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

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

    Definition Classes
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  13. final def isInstanceOf[T0]: Boolean

    Definition Classes
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  14. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  15. final def notify(): Unit

    Definition Classes
    AnyRef

  16. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  17. def predict(features: Vector): Double

    Predict the class for a feature vector.

    Predict the class for a feature vector.

    features

    feature vector

    returns

    predicted class

  18. def predictProbability(features: Vector): Double

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

    Predict the class and probability for a feature vector.

    Predict the class and probability for a feature vector.

    features

    feature vector

    returns

    (predicted class, probability of class)

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

    Definition Classes
    AnyRef

  21. final def wait(): Unit

    Definition Classes
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    @throws( ... )

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

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

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