Class/Object

org.apache.spark.mllib.classification

NaiveBayesModel

Related Docs: object NaiveBayesModel | package classification

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class NaiveBayesModel extends ClassificationModel with Serializable with Saveable

Model for Naive Bayes Classifiers.

Annotations
@Since( "0.9.0" )
Source
NaiveBayes.scala
Linear Supertypes
Saveable, ClassificationModel, Serializable, Serializable, AnyRef, Any
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Inherited
  1. NaiveBayesModel
  2. Saveable
  3. ClassificationModel
  4. Serializable
  5. Serializable
  6. AnyRef
  7. Any
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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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  3. final def ==(arg0: Any): Boolean

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    Definition Classes
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  4. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  5. def clone(): AnyRef

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    Attributes
    protected[java.lang]
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    AnyRef
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    @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
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  7. def equals(arg0: Any): Boolean

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    Definition Classes
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  8. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  9. def formatVersion: String

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    Current version of model save/load format.

    Current version of model save/load format.

    Attributes
    protected
    Definition Classes
    NaiveBayesModelSaveable
  10. final def getClass(): Class[_]

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    Definition Classes
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  11. def hashCode(): Int

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  12. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  13. val labels: Array[Double]

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    list of labels

    list of labels

    Annotations
    @Since( "1.0.0" )
  14. val modelType: String

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    The type of NB model to fit can be "multinomial" or "bernoulli"

    The type of NB model to fit can be "multinomial" or "bernoulli"

    Annotations
    @Since( "1.4.0" )
  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 pi: Array[Double]

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

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

    Annotations
    @Since( "0.9.0" )
  19. def predict(testData: Vector): Double

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    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
    Annotations
    @Since( "1.0.0" )
  20. def predict(testData: RDD[Vector]): RDD[Double]

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    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
    Annotations
    @Since( "1.0.0" )
  21. def predict(testData: JavaRDD[Vector]): JavaRDD[Double]

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    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
    Annotations
    @Since( "1.0.0" )
  22. def predictProbabilities(testData: Vector): Vector

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    Predict posterior class probabilities for a single data point using the model trained.

    Predict posterior class probabilities for a single data point using the model trained.

    testData

    array representing a single data point

    returns

    predicted posterior class probabilities from the trained model, in the same order as class labels

    Annotations
    @Since( "1.5.0" )
  23. def predictProbabilities(testData: RDD[Vector]): RDD[Vector]

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    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[Vector] where each entry contains the predicted posterior class probabilities, in the same order as class labels

    Annotations
    @Since( "1.5.0" )
  24. def save(sc: SparkContext, path: String): Unit

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    Save this model to the given path.

    Save this model to the given path.

    This saves:

    • human-readable (JSON) model metadata to path/metadata/
    • Parquet formatted data to path/data/

    The model may be loaded using Loader.load.

    sc

    Spark context used to save model data.

    path

    Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.

    Definition Classes
    NaiveBayesModelSaveable
    Annotations
    @Since( "1.3.0" )
  25. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  26. val theta: Array[Array[Double]]

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

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

    Annotations
    @Since( "0.9.0" )
  27. def toString(): String

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    Definition Classes
    AnyRef → Any
  28. final def wait(): Unit

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    @throws( ... )
  29. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  30. final def wait(arg0: Long): Unit

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    @throws( ... )

Inherited from Saveable

Inherited from ClassificationModel

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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