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    root
  • package org
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    root
  • package apache
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    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

RegressionMetrics

class RegressionMetrics extends Logging

Evaluator for regression.

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@Since( "1.2.0" )
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Instance Constructors

  1. new RegressionMetrics(predictionAndObservations: RDD[(Double, Double)])
    Annotations
    @Since( "1.2.0" )
  2. new RegressionMetrics(predictionAndObservations: RDD[(Double, Double)], throughOrigin: Boolean)

    predictionAndObservations

    an RDD of (prediction, observation) pairs

    throughOrigin

    True if the regression is through the origin. For example, in linear regression, it will be true without fitting intercept.

    Annotations
    @Since( "2.0.0" )

Value Members

  1. final def !=(arg0: Any): Boolean
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  5. def clone(): AnyRef
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  6. final def eq(arg0: AnyRef): Boolean
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  7. def equals(arg0: Any): Boolean
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  8. def explainedVariance: Double

    Returns the variance explained by regression.

    Returns the variance explained by regression. explainedVariance = $\sum_i (\hat{y_i} - \bar{y})2 / n$

    Annotations
    @Since( "1.2.0" )
    See also

    Fraction of variance unexplained (Wikipedia)

  9. def finalize(): Unit
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  10. final def getClass(): Class[_]
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  11. def hashCode(): Int
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  12. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean
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  13. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
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  14. final def isInstanceOf[T0]: Boolean
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  15. def isTraceEnabled(): Boolean
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  16. def log: Logger
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  17. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
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  18. def logDebug(msg: ⇒ String): Unit
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  19. def logError(msg: ⇒ String, throwable: Throwable): Unit
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  20. def logError(msg: ⇒ String): Unit
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  21. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
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  22. def logInfo(msg: ⇒ String): Unit
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  23. def logName: String
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  24. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
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  25. def logTrace(msg: ⇒ String): Unit
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  26. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
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  27. def logWarning(msg: ⇒ String): Unit
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  28. def meanAbsoluteError: Double

    Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.

    Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.

    Annotations
    @Since( "1.2.0" )
  29. def meanSquaredError: Double

    Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.

    Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.

    Annotations
    @Since( "1.2.0" )
  30. final def ne(arg0: AnyRef): Boolean
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  31. final def notify(): Unit
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  32. final def notifyAll(): Unit
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  33. def r2: Double

    Returns R2, the unadjusted coefficient of determination.

    Returns R2, the unadjusted coefficient of determination.

    Annotations
    @Since( "1.2.0" )
    See also

    Coefficient of determination (Wikipedia) In case of regression through the origin, the definition of R2 is to be modified.

    J. G. Eisenhauer, Regression through the Origin. Teaching Statistics 25, 76-80 (2003)

  34. def rootMeanSquaredError: Double

    Returns the root mean squared error, which is defined as the square root of the mean squared error.

    Returns the root mean squared error, which is defined as the square root of the mean squared error.

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    @Since( "1.2.0" )
  35. final def synchronized[T0](arg0: ⇒ T0): T0
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  36. def toString(): String
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  37. final def wait(): Unit
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  38. final def wait(arg0: Long, arg1: Int): Unit
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  39. final def wait(arg0: Long): Unit
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