NaiveBayesModel

Value Members

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

   Definition Classes

   AnyRef

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

   Definition Classes

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3. final def ##(): Int

   Definition Classes

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

   Definition Classes

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

   Definition Classes

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6. final def asInstanceOf[T0]: T0

   Definition Classes

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7. def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

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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]

    Definition Classes

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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

    NaiveBayesModel → Saveable

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

    Definition Classes

    AnyRef → Any

13. def hashCode(): Int

    Definition Classes

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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

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18. final def notify(): Unit

    Definition Classes

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19. final def notifyAll(): Unit

    Definition Classes

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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

    NaiveBayesModel → ClassificationModel

    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

    NaiveBayesModel → ClassificationModel

    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

    NaiveBayesModel → Saveable

    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

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    @throws( ... )