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 impurity
    Definition Classes
    tree
  • Entropy
  • Gini
  • Impurity
  • Variance

object Gini extends Impurity

Class for calculating the Gini impurity (http://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity) during multiclass classification.

Annotations
@Since( "1.0.0" )
Source
Gini.scala
Linear Supertypes
Impurity, Serializable, Serializable, AnyRef, Any
Ordering
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Inherited
  1. Gini
  2. Impurity
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
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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. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def calculate(count: Double, sum: Double, sumSquares: Double): Double

    variance calculation

    variance calculation

    count

    number of instances

    sum

    sum of labels

    sumSquares

    summation of squares of the labels

    returns

    information value, or 0 if count = 0

    Definition Classes
    Gini → Impurity
    Annotations
    @Since( "1.0.0" )
  6. def calculate(counts: Array[Double], totalCount: Double): Double

    information calculation for multiclass classification

    information calculation for multiclass classification

    counts

    Array[Double] with counts for each label

    totalCount

    sum of counts for all labels

    returns

    information value, or 0 if totalCount = 0

    Definition Classes
    Gini → Impurity
    Annotations
    @Since( "1.1.0" )
  7. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  8. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  10. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  11. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  12. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  13. def instance: Gini.this.type

    Get this impurity instance.

    Get this impurity instance. This is useful for passing impurity parameters to a Strategy in Java.

    Annotations
    @Since( "1.1.0" )
  14. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  15. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  16. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  17. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  18. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  19. def toString(): String
    Definition Classes
    AnyRef → Any
  20. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  21. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  22. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()

Inherited from Impurity

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped