edu.arizona.sista.learning

BaggingClassifier

Related Docs: object BaggingClassifier | package learning

class BaggingClassifier[L, F] extends Classifier[L, F]

Classifier that implements bagging over another Classifier Created by dfried, mihais Date: 4/25/14

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Classifier[L, F], AnyRef, Any
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Instance Constructors

  1. new BaggingClassifier(baseClassifierFactory: () ⇒ Classifier[L, F], N: Int, random: Random = null)

Value Members

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

    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    AnyRef → Any

  4. val N: Int

  5. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  6. val baseClassifierFactory: () ⇒ Classifier[L, F]

  7. def classOf(d: Datum[L, F]): L

    Returns the argmax for this datum

    Returns the argmax for this datum

    Definition Classes
    BaggingClassifier → Classifier

  8. val classifiers: Array[Classifier[L, F]]

  9. def clone(): AnyRef

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

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

    Definition Classes
    AnyRef

  11. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  12. def finalize(): Unit

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

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

    Definition Classes
    AnyRef → Any

  14. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  15. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  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 random: Random

  20. def saveTo(fn: String): Unit

    Saves the current model to a file

    Saves the current model to a file

    Definition Classes
    BaggingClassifier → Classifier

  21. def saveTo(writer: Writer): Unit

    Saves the current model to a file

    Saves the current model to a file

    Definition Classes
    BaggingClassifier → Classifier

  22. def scoresOf(d: Datum[L, F]): Counter[L]

    Returns the scores of all possible labels for this datum Convention: if the classifier can return probabilities, these must be probabilities

    Returns the scores of all possible labels for this datum Convention: if the classifier can return probabilities, these must be probabilities

    Definition Classes
    BaggingClassifier → Classifier

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

    Definition Classes
    AnyRef

  24. def toString(): String

    Definition Classes
    AnyRef → Any

  25. def train(dataset: Dataset[L, F], indices: Array[Int]): Unit

    Trains the classifier on the given dataset

    Trains the classifier on the given dataset

    Definition Classes
    BaggingClassifier → Classifier

  26. def train(dataset: Dataset[L, F], spans: Option[Iterable[(Int, Int)]] = None): Unit

    Trains the classifier on the given dataset spans is useful during cross validation

    Trains the classifier on the given dataset spans is useful during cross validation

    Definition Classes
    Classifier

  27. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Classifier[L, F]

Inherited from AnyRef

Inherited from Any

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