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 evaluation
    Definition Classes
    mllib
  • BinaryClassificationMetrics
  • MulticlassMetrics
  • MultilabelMetrics
  • RankingMetrics
  • RegressionMetrics
c

org.apache.spark.mllib.evaluation

MulticlassMetrics

class MulticlassMetrics extends AnyRef

Evaluator for multiclass classification.

Annotations
@Since( "1.1.0" )
Linear Supertypes
AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. MulticlassMetrics
  2. AnyRef
  3. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new MulticlassMetrics(predAndLabelsWithOptWeight: RDD[_ <: Product])

    predAndLabelsWithOptWeight

    an RDD of (prediction, label, weight) or (prediction, label) pairs.

    Annotations
    @Since( "1.1.0" )

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. lazy val accuracy: Double

    Returns accuracy (equals to the total number of correctly classified instances out of the total number of instances.)

    Returns accuracy (equals to the total number of correctly classified instances out of the total number of instances.)

    Annotations
    @Since( "2.0.0" )
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  7. def confusionMatrix: Matrix

    Returns confusion matrix: predicted classes are in columns, they are ordered by class label ascending, as in "labels"

    Returns confusion matrix: predicted classes are in columns, they are ordered by class label ascending, as in "labels"

    Annotations
    @Since( "1.1.0" )
  8. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  10. def fMeasure(label: Double): Double

    Returns f1-measure for a given label (category)

    Returns f1-measure for a given label (category)

    label

    the label.

    Annotations
    @Since( "1.1.0" )
  11. def fMeasure(label: Double, beta: Double): Double

    Returns f-measure for a given label (category)

    Returns f-measure for a given label (category)

    label

    the label.

    beta

    the beta parameter.

    Annotations
    @Since( "1.1.0" )
  12. def falsePositiveRate(label: Double): Double

    Returns false positive rate for a given label (category)

    Returns false positive rate for a given label (category)

    label

    the label.

    Annotations
    @Since( "1.1.0" )
  13. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  14. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  15. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  16. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  17. lazy val labels: Array[Double]

    Returns the sequence of labels in ascending order

    Returns the sequence of labels in ascending order

    Annotations
    @Since( "1.1.0" )
  18. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  19. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  20. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  21. def precision(label: Double): Double

    Returns precision for a given label (category)

    Returns precision for a given label (category)

    label

    the label.

    Annotations
    @Since( "1.1.0" )
  22. val predLabelsWeight: RDD[(Double, Double, Double)]
  23. def recall(label: Double): Double

    Returns recall for a given label (category)

    Returns recall for a given label (category)

    label

    the label.

    Annotations
    @Since( "1.1.0" )
  24. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  25. def toString(): String
    Definition Classes
    AnyRef → Any
  26. def truePositiveRate(label: Double): Double

    Returns true positive rate for a given label (category)

    Returns true positive rate for a given label (category)

    label

    the label.

    Annotations
    @Since( "1.1.0" )
  27. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  28. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  29. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  30. def weightedFMeasure(beta: Double): Double

    Returns weighted averaged f-measure

    Returns weighted averaged f-measure

    beta

    the beta parameter.

    Annotations
    @Since( "1.1.0" )
  31. lazy val weightedFMeasure: Double

    Returns weighted averaged f1-measure

    Returns weighted averaged f1-measure

    Annotations
    @Since( "1.1.0" )
  32. lazy val weightedFalsePositiveRate: Double

    Returns weighted false positive rate

    Returns weighted false positive rate

    Annotations
    @Since( "1.1.0" )
  33. lazy val weightedPrecision: Double

    Returns weighted averaged precision

    Returns weighted averaged precision

    Annotations
    @Since( "1.1.0" )
  34. lazy val weightedRecall: Double

    Returns weighted averaged recall (equals to precision, recall and f-measure)

    Returns weighted averaged recall (equals to precision, recall and f-measure)

    Annotations
    @Since( "1.1.0" )
  35. lazy val weightedTruePositiveRate: Double

    Returns weighted true positive rate (equals to precision, recall and f-measure)

    Returns weighted true positive rate (equals to precision, recall and f-measure)

    Annotations
    @Since( "1.1.0" )

Inherited from AnyRef

Inherited from Any

Ungrouped