org.apache.spark.mllib.classification

NaiveBayesModel

class NaiveBayesModel extends ClassificationModel with Serializable with Saveable

Model for Naive Bayes Classifiers.

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@Since( "0.9.0" )
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Saveable, ClassificationModel, Serializable, Serializable, AnyRef, Any
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  1. NaiveBayesModel
  2. Saveable
  3. ClassificationModel
  4. Serializable
  5. Serializable
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  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. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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

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

    Definition Classes
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  10. def finalize(): Unit

    Attributes
    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  11. def formatVersion: String

    Current version of model save/load format.

    Current version of model save/load format.

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

    Definition Classes
    AnyRef → Any
  13. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  14. final def isInstanceOf[T0]: Boolean

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

    list of labels

    list of labels

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

    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" )
  17. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  18. final def notify(): Unit

    Definition Classes
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  19. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  20. val pi: Array[Double]

    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" )
  21. 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
    Annotations
    @Since( "1.0.0" )
  22. 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
    Annotations
    @Since( "1.0.0" )
  23. 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
    Annotations
    @Since( "1.0.0" )
  24. def predictProbabilities(testData: Vector): Vector

    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" )
  25. def predictProbabilities(testData: RDD[Vector]): RDD[Vector]

    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" )
  26. def save(sc: SparkContext, path: String): Unit

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

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

    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" )
  29. def toString(): String

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

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

    Definition Classes
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    @throws( ... )
  32. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
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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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