Value Members

1.  final def !=(arg0: Any): Boolean

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2.  final def ##(): Int

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3.  final def ==(arg0: Any): Boolean

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4.  final def asInstanceOf[T0]: T0

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5.  def clone(): AnyRef

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   protected[lang]

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6.  final def eq(arg0: AnyRef): Boolean

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7.  def equals(arg0: Any): Boolean

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8.  def finalize(): Unit

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   @throws( classOf[java.lang.Throwable] )

9.  final def getClass(): Class[_]

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   @native()

10.  def hashCode(): Int

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    @native()

11.  final def isInstanceOf[T0]: Boolean

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12.  val labels: Array[Double]

    Annotations

    @Since( "1.0.0" )

13.  val modelType: String

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    @Since( "1.4.0" )

14.  final def ne(arg0: AnyRef): Boolean

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15.  final def notify(): Unit

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    @native()

16.  final def notifyAll(): Unit

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    @native()

17.  val pi: Array[Double]

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    @Since( "0.9.0" )

18.  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" )

19.  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" )

20.  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" )

21.  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" )

22.  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" )

23.  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" )

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

    Definition Classes

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25.  val theta: Array[Array[Double]]

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    @Since( "0.9.0" )

26.  def toString(): String

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27.  final def wait(): Unit

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    @throws( ... )

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

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    @throws( ... )

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

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    @throws( ... ) @native()