Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    org
  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

    Definition Classes
    apache
  • package mllib

    RDD-based machine learning APIs (in maintenance mode).

    RDD-based machine learning APIs (in maintenance mode).

    The spark.mllib package is in maintenance mode as of the Spark 2.0.0 release to encourage migration to the DataFrame-based APIs under the org.apache.spark.ml package. While in maintenance mode,

    • no new features in the RDD-based spark.mllib package will be accepted, unless they block implementing new features in the DataFrame-based spark.ml package;
    • bug fixes in the RDD-based APIs will still be accepted.

    The developers will continue adding more features to the DataFrame-based APIs in the 2.x series to reach feature parity with the RDD-based APIs. And once we reach feature parity, this package will be deprecated.

    Definition Classes
    spark
    See also

    SPARK-4591 to track the progress of feature parity

  • package classification
    Definition Classes
    mllib
  • ClassificationModel
  • LogisticRegressionModel
  • LogisticRegressionWithLBFGS
  • LogisticRegressionWithSGD
  • NaiveBayes
  • NaiveBayesModel
  • SVMModel
  • SVMWithSGD
  • StreamingLogisticRegressionWithSGD

class NaiveBayes extends Serializable with Logging

Trains a Naive Bayes model given an RDD of (label, features) pairs.

This is the Multinomial NB (see here) which can handle all kinds of discrete data. For example, by converting documents into TF-IDF vectors, it can be used for document classification. By making every vector a 0-1 vector, it can also be used as Bernoulli NB (see here). The input feature values must be nonnegative.

Annotations
@Since("0.9.0")
Source
NaiveBayes.scala
Linear Supertypes
Logging, Serializable, AnyRef, Any
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Visibility
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  2. Protected

Instance Constructors

  1. new NaiveBayes()
    Annotations
    @Since("0.9.0")
  2. new NaiveBayes(lambda: Double)
    Annotations
    @Since("1.4.0")

Type Members

  1. implicit class LogStringContext extends AnyRef
    Definition Classes
    Logging

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def MDC(key: LogKey, value: Any): MDC
    Attributes
    protected
    Definition Classes
    Logging
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  8. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  9. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  10. def getLambda: Double

    Get the smoothing parameter.

    Get the smoothing parameter.

    Annotations
    @Since("1.4.0")
  11. def getModelType: String

    Get the model type.

    Get the model type.

    Annotations
    @Since("1.4.0")
  12. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  13. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  14. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
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    protected
    Definition Classes
    Logging
  15. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  16. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  17. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  18. def logBasedOnLevel(level: Level)(f: => MessageWithContext): Unit
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    protected
    Definition Classes
    Logging
  19. def logDebug(msg: => String, throwable: Throwable): Unit
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    protected
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    Logging
  20. def logDebug(entry: LogEntry, throwable: Throwable): Unit
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    protected
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    Logging
  21. def logDebug(entry: LogEntry): Unit
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    protected
    Definition Classes
    Logging
  22. def logDebug(msg: => String): Unit
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    protected
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    Logging
  23. def logError(msg: => String, throwable: Throwable): Unit
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    Logging
  24. def logError(entry: LogEntry, throwable: Throwable): Unit
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    Logging
  25. def logError(entry: LogEntry): Unit
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    Logging
  26. def logError(msg: => String): Unit
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    Logging
  27. def logInfo(msg: => String, throwable: Throwable): Unit
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    Logging
  28. def logInfo(entry: LogEntry, throwable: Throwable): Unit
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    Logging
  29. def logInfo(entry: LogEntry): Unit
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    Logging
  30. def logInfo(msg: => String): Unit
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    Logging
  31. def logName: String
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    Logging
  32. def logTrace(msg: => String, throwable: Throwable): Unit
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    Logging
  33. def logTrace(entry: LogEntry, throwable: Throwable): Unit
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    Logging
  34. def logTrace(entry: LogEntry): Unit
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    Logging
  35. def logTrace(msg: => String): Unit
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    protected
    Definition Classes
    Logging
  36. def logWarning(msg: => String, throwable: Throwable): Unit
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    Logging
  37. def logWarning(entry: LogEntry, throwable: Throwable): Unit
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    Logging
  38. def logWarning(entry: LogEntry): Unit
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    Logging
  39. def logWarning(msg: => String): Unit
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    protected
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    Logging
  40. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  41. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  42. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  43. def run(data: RDD[LabeledPoint]): NaiveBayesModel

    Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.

    Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.

    data

    RDD of org.apache.spark.mllib.regression.LabeledPoint.

    Annotations
    @Since("0.9.0")
  44. def setLambda(lambda: Double): NaiveBayes

    Set the smoothing parameter.

    Set the smoothing parameter. Default: 1.0.

    Annotations
    @Since("0.9.0")
  45. def setModelType(modelType: String): NaiveBayes

    Set the model type using a string (case-sensitive).

    Set the model type using a string (case-sensitive). Supported options: "multinomial" (default) and "bernoulli".

    Annotations
    @Since("1.4.0")
  46. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  47. def toString(): String
    Definition Classes
    AnyRef → Any
  48. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  49. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
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    @throws(classOf[java.lang.InterruptedException]) @native()
  50. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  51. def withLogContext(context: Map[String, String])(body: => Unit): Unit
    Attributes
    protected
    Definition Classes
    Logging

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

    (Since version 9)

Inherited from Logging

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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