org.apache.spark.ml.regression

LinearRegressionTrainingSummary

class LinearRegressionTrainingSummary extends LinearRegressionSummary

:: Experimental :: Linear regression training results.

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@Experimental()
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LinearRegressionSummary, Serializable, Serializable, AnyRef, Any
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  1. final def !=(arg0: AnyRef): Boolean

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  3. final def ##(): Int

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  8. final def eq(arg0: AnyRef): Boolean

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  9. def equals(arg0: Any): Boolean

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  10. 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

    Definition Classes
    LinearRegressionSummary
  11. val featuresCol: String

  12. def finalize(): Unit

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  13. final def getClass(): Class[_]

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. 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.

    Definition Classes
    LinearRegressionSummary
  17. 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.

    Definition Classes
    LinearRegressionSummary
  18. final def ne(arg0: AnyRef): Boolean

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  19. final def notify(): Unit

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  20. final def notifyAll(): Unit

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  21. val objectiveHistory: Array[Double]

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

  22. val r2: Double

    Returns R2, the coefficient of determination.

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

    Definition Classes
    LinearRegressionSummary
  23. lazy val residuals: DataFrame

    Residuals (label - predicted value)

    Residuals (label - predicted value)

    Definition Classes
    LinearRegressionSummary
  24. 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.

    Definition Classes
    LinearRegressionSummary
  25. final def synchronized[T0](arg0: ⇒ T0): T0

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  26. def toString(): String

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  27. val totalIterations: Int

    Number of training iterations until termination

  28. final def wait(): Unit

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

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

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