org.apache.spark.mllib.classification

NaiveBayesModel

class NaiveBayesModel extends ClassificationModel with Serializable

Model for Naive Bayes Classifiers.

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ClassificationModel, Serializable, Serializable, AnyRef, Any
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  2. ClassificationModel
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  1. final def !=(arg0: AnyRef): Boolean

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

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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

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  12. def hashCode(): Int

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  13. final def isInstanceOf[T0]: Boolean

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  14. val labels: Array[Double]

    list of labels

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

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  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

    Definition Classes
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  18. val pi: Array[Double]

    log of class priors, whose dimension is C, number of labels

  19. def predict(testData: Vector): Double

    Predict values for a single data point using the model trained.

    Predict values for a single data point using the model trained.

    testData

    array representing a single data point

    returns

    predicted category from the trained model

    Definition Classes
    NaiveBayesModelClassificationModel
  20. def predict(testData: RDD[Vector]): RDD[Double]

    Predict values for the given data set using the model trained.

    Predict values for the given data set using the model trained.

    testData

    RDD representing data points to be predicted

    returns

    an RDD[Double] where each entry contains the corresponding prediction

    Definition Classes
    NaiveBayesModelClassificationModel
  21. def predict(testData: JavaRDD[Vector]): JavaRDD[Double]

    Predict values for examples stored in a JavaRDD.

    Predict values for examples stored in a JavaRDD.

    testData

    JavaRDD representing data points to be predicted

    returns

    a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction

    Definition Classes
    ClassificationModel
  22. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
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  23. val theta: Array[Array[Double]]

    log of class conditional probabilities, whose dimension is C-by-D, where D is number of features

  24. def toString(): String

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  25. final def wait(): Unit

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

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

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Inherited from ClassificationModel

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