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 tree

    This package contains the default implementation of the decision tree algorithm, which supports:

    This package contains the default implementation of the decision tree algorithm, which supports:

    • binary classification,
    • regression,
    • information loss calculation with entropy and Gini for classification and variance for regression,
    • both continuous and categorical features.
    Definition Classes
    mllib
  • package model
    Definition Classes
    tree
  • DecisionTreeModel
  • GradientBoostedTreesModel
  • InformationGainStats
  • Node
  • Predict
  • RandomForestModel
  • Split

case class Split(feature: Int, threshold: Double, featureType: FeatureType, categories: List[Double]) extends Product with Serializable

Split applied to a feature

feature

feature index

threshold

Threshold for continuous feature. Split left if feature is less than or equal to threshold, else right.

featureType

type of feature -- categorical or continuous

categories

Split left if categorical feature value is in this set, else right.

Annotations
@Since( "1.0.0" )
Source
Split.scala
Linear Supertypes
Serializable, Serializable, Product, Equals, AnyRef, Any
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  1. Split
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Instance Constructors

  1. new Split(feature: Int, threshold: Double, featureType: FeatureType, categories: List[Double])

    feature

    feature index

    threshold

    Threshold for continuous feature. Split left if feature is less than or equal to threshold, else right.

    featureType

    type of feature -- categorical or continuous

    categories

    Split left if categorical feature value is in this set, else right.

Value Members

  1. val categories: List[Double]
    Annotations
    @Since( "1.0.0" )
  2. val feature: Int
    Annotations
    @Since( "1.0.0" )
  3. val featureType: FeatureType
    Annotations
    @Since( "1.0.0" )
  4. val threshold: Double
    Annotations
    @Since( "1.0.0" )
  5. def toString(): String
    Definition Classes
    Split → AnyRef → Any