org.apache.spark.streamdm.classifiers.meta

Bagging

class Bagging extends Classifier

The Bagging classifier trains an ensemble of classifier to improve performance. It is based on doing sampling with replacement at the input of each classifier.

It uses the following options:

Linear Supertypes
Classifier, Learner, Serializable, Configurable, Serializable, AnyRef, Any
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Inherited
  1. Bagging
  2. Classifier
  3. Learner
  4. Serializable
  5. Configurable
  6. Serializable
  7. AnyRef
  8. Any
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Instance Constructors

  1. new Bagging()

Type Members

  1. type T = LinearModel

    Definition Classes
    Bagging → Learner

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. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  7. val baseClassifierOption: ClassOption

  8. val classifierRandom: Random

  9. var classifiers: Array[Classifier]

  10. def clone(): AnyRef

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

  11. def ensemblePredict(example: Example): Double

  12. val ensembleSizeOption: IntOption

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

    Definition Classes
    AnyRef

  14. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  15. var exampleLearnerSpecification: ExampleSpecification

  16. def finalize(): Unit

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

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

    Definition Classes
    AnyRef → Any

  18. def getModel: LinearModel

    Gets the current Model used for the Learner.

    Gets the current Model used for the Learner.

    returns

    the Model object used for training

    Definition Classes
    Bagging → Learner

  19. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  20. def init(exampleSpecification: ExampleSpecification): Unit

    Init the model based on the algorithm implemented in the learner.

    Init the model based on the algorithm implemented in the learner.

    exampleSpecification

    the ExampleSpecification of the input stream.

    Definition Classes
    Bagging → Learner

  21. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

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

    Definition Classes
    AnyRef

  23. final def notify(): Unit

    Definition Classes
    AnyRef

  24. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  25. def numberClasses(): Integer

  26. def predict(input: DStream[Example]): DStream[(Example, Double)]

    Definition Classes
    Bagging → Classifier

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

    Definition Classes
    AnyRef

  28. def toString(): String

    Definition Classes
    AnyRef → Any

  29. def train(input: DStream[Example]): Unit

    Train the model based on the algorithm implemented in the learner, from the stream of Examples given for training.

    Train the model based on the algorithm implemented in the learner, from the stream of Examples given for training.

    input

    a stream of Examples

    Definition Classes
    Bagging → Learner

  30. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Classifier

Inherited from Learner

Inherited from Serializable

Inherited from Configurable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped