Class

org.apache.spark.mllib.evaluation

RegressionMetrics

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class RegressionMetrics extends Logging

Evaluator for regression.

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@Since( "1.2.0" )
Source
RegressionMetrics.scala
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Instance Constructors

  1. new RegressionMetrics(predictionAndObservations: RDD[(Double, Double)])

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

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    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.

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    @Since( "2.0.0" )

Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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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

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    Returns the variance explained by regression.

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

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    @Since( "1.2.0" )
    See also

    https://en.wikipedia.org/wiki/Fraction_of_variance_unexplained

  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): Unit

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  13. final def isInstanceOf[T0]: Boolean

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  14. def isTraceEnabled(): Boolean

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  15. def log: Logger

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  16. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  17. def logDebug(msg: ⇒ String): Unit

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  18. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  19. def logError(msg: ⇒ String): Unit

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  20. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  21. def logInfo(msg: ⇒ String): Unit

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  22. def logName: String

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  23. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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  24. def logTrace(msg: ⇒ String): Unit

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  25. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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  26. def logWarning(msg: ⇒ String): Unit

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  27. def meanAbsoluteError: Double

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    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.

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    @Since( "1.2.0" )
  28. def meanSquaredError: Double

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    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.

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    @Since( "1.2.0" )
  29. final def ne(arg0: AnyRef): Boolean

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  30. final def notify(): Unit

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  31. final def notifyAll(): Unit

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  32. def r2: Double

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    Returns R2, the unadjusted coefficient of determination.

    Returns R2, the unadjusted coefficient of determination.

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    @Since( "1.2.0" )
    See also

    J. G. Eisenhauer, Regression through the Origin. Teaching Statistics 25, 76-80 (2003) https://online.stat.psu.edu/~ajw13/stat501/SpecialTopics/Reg_thru_origin.pdf

    http://en.wikipedia.org/wiki/Coefficient_of_determination In case of regression through the origin, the definition of R2 is to be modified.

  33. def rootMeanSquaredError: Double

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    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" )
  34. final def synchronized[T0](arg0: ⇒ T0): T0

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  35. def toString(): String

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  36. final def wait(): Unit

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  37. final def wait(arg0: Long, arg1: Int): Unit

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  38. final def wait(arg0: Long): Unit

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