org.apache.spark.ml.regression

LinearRegressionTrainingSummary

class LinearRegressionTrainingSummary extends LinearRegressionSummary

:: Experimental :: Linear regression training results. Currently, the training summary ignores the training coefficients except for the objective trace.

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@Since( "1.5.0" ) @Experimental()
Source
LinearRegression.scala
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LinearRegressionSummary, Serializable, Serializable, AnyRef, Any
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  1. LinearRegressionTrainingSummary
  2. LinearRegressionSummary
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  1. final def !=(arg0: AnyRef): Boolean

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

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
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    @throws( ... )
  8. lazy val coefficientStandardErrors: Array[Double]

    Standard error of estimated coefficients and intercept.

    Standard error of estimated coefficients and intercept.

    Definition Classes
    LinearRegressionSummary
  9. lazy val devianceResiduals: Array[Double]

    The weighted residuals, the usual residuals rescaled by the square root of the instance weights.

    The weighted residuals, the usual residuals rescaled by the square root of the instance weights.

    Definition Classes
    LinearRegressionSummary
  10. final def eq(arg0: AnyRef): Boolean

    Definition Classes
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  11. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  12. val explainedVariance: Double

    Returns the explained variance regression score.

    Returns the explained variance regression score. explainedVariance = 1 - variance(y - \hat{y}) / variance(y) Reference: http://en.wikipedia.org/wiki/Explained_variation

    Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.

    Definition Classes
    LinearRegressionSummary
    Annotations
    @Since( "1.5.0" )
  13. val featuresCol: String

  14. def finalize(): Unit

    Attributes
    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  15. final def getClass(): Class[_]

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  16. def hashCode(): Int

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  17. final def isInstanceOf[T0]: Boolean

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  18. val 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.

    Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.

    Definition Classes
    LinearRegressionSummary
    Annotations
    @Since( "1.5.0" )
  19. val 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.

    Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.

    Definition Classes
    LinearRegressionSummary
    Annotations
    @Since( "1.5.0" )
  20. final def ne(arg0: AnyRef): Boolean

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  21. final def notify(): Unit

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  22. final def notifyAll(): Unit

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  23. lazy val numInstances: Long

    Number of instances in DataFrame predictions

    Number of instances in DataFrame predictions

    Definition Classes
    LinearRegressionSummary
  24. val objectiveHistory: Array[Double]

    objective function (scaled loss + regularization) at each iteration.

  25. lazy val pValues: Array[Double]

    Two-sided p-value of estimated coefficients and intercept.

    Two-sided p-value of estimated coefficients and intercept.

    Definition Classes
    LinearRegressionSummary
  26. val r2: Double

    Returns R2, the coefficient of determination.

    Returns R2, the coefficient of determination. Reference: http://en.wikipedia.org/wiki/Coefficient_of_determination

    Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.

    Definition Classes
    LinearRegressionSummary
    Annotations
    @Since( "1.5.0" )
  27. lazy val residuals: DataFrame

    Residuals (label - predicted value)

    Residuals (label - predicted value)

    Definition Classes
    LinearRegressionSummary
    Annotations
    @Since( "1.5.0" )
  28. val 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.

    Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.

    Definition Classes
    LinearRegressionSummary
    Annotations
    @Since( "1.5.0" )
  29. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  30. lazy val tValues: Array[Double]

    T-statistic of estimated coefficients and intercept.

    T-statistic of estimated coefficients and intercept.

    Definition Classes
    LinearRegressionSummary
  31. def toString(): String

    Definition Classes
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  32. val totalIterations: Int

    Number of training iterations until termination

    Number of training iterations until termination

    Annotations
    @Since( "1.5.0" )
  33. final def wait(): Unit

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    @throws( ... )
  34. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  35. final def wait(arg0: Long): Unit

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    @throws( ... )

Inherited from LinearRegressionSummary

Inherited from Serializable

Inherited from Serializable

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