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

object NaiveBayes extends Serializable

Top-level methods for calling naive Bayes.

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@Since("0.9.0")
Source
NaiveBayes.scala
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  16. def train(input: RDD[LabeledPoint], lambda: Double, modelType: String): NaiveBayesModel

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

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

    The model type can be set to either Multinomial NB (see here) or Bernoulli NB (see here). The Multinomial NB can handle discrete count data and can be called by setting the model type to "multinomial". For example, it can be used with word counts or TF_IDF vectors of documents. The Bernoulli model fits presence or absence (0-1) counts. By making every vector a 0-1 vector and setting the model type to "bernoulli", the fits and predicts as Bernoulli NB.

    input

    RDD of (label, array of features) pairs. Every vector should be a frequency vector or a count vector.

    lambda

    The smoothing parameter

    modelType

    The type of NB model to fit from the enumeration NaiveBayesModels, can be multinomial or bernoulli

    Annotations
    @Since("1.4.0")
  17. def train(input: RDD[LabeledPoint], lambda: Double): NaiveBayesModel

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

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

    This is the default 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.

    input

    RDD of (label, array of features) pairs. Every vector should be a frequency vector or a count vector.

    lambda

    The smoothing parameter

    Annotations
    @Since("0.9.0")
  18. def train(input: RDD[LabeledPoint]): NaiveBayesModel

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

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

    This is the default 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.

    This version of the method uses a default smoothing parameter of 1.0.

    input

    RDD of (label, array of features) pairs. Every vector should be a frequency vector or a count vector.

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    @Since("0.9.0")
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