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c

org.apache.spark.ml.regression

LinearRegressionSummary

class LinearRegressionSummary extends Serializable

:: Experimental :: Linear regression results evaluated on a dataset.

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@Since( "1.5.0" ) @Experimental()
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LinearRegressionTrainingSummary
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  1. final def !=(arg0: Any): Boolean
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    @native() @throws( ... )
  6. lazy val coefficientStandardErrors: Array[Double]

    Standard error of estimated coefficients and intercept.

    Standard error of estimated coefficients and intercept. This value is only available when using the "normal" solver.

    If LinearRegression.fitIntercept is set to true, then the last element returned corresponds to the intercept.

    See also

    LinearRegression.solver

  7. val degreesOfFreedom: Long

    Degrees of freedom

    Degrees of freedom

    Annotations
    @Since( "2.2.0" )
  8. lazy val devianceResiduals: Array[Double]

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

  9. final def eq(arg0: AnyRef): Boolean
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  10. def equals(arg0: Any): Boolean
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  11. val explainedVariance: Double

    Returns the explained variance regression score.

    Returns the explained variance regression score. explainedVariance = 1 - variance(y - \hat{y}) / variance(y) Reference: Wikipedia explain variation

    Annotations
    @Since( "1.5.0" )
    Note

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

  12. val featuresCol: String
  13. def finalize(): Unit
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  15. def hashCode(): Int
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  16. final def isInstanceOf[T0]: Boolean
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  17. val labelCol: String
  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.

    Annotations
    @Since( "1.5.0" )
    Note

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

  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.

    Annotations
    @Since( "1.5.0" )
    Note

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

  20. final def ne(arg0: AnyRef): Boolean
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  21. final def notify(): Unit
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    @native()
  22. final def notifyAll(): Unit
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  23. lazy val numInstances: Long

    Number of instances in DataFrame predictions

  24. lazy val pValues: Array[Double]

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

    Two-sided p-value of estimated coefficients and intercept. This value is only available when using the "normal" solver.

    If LinearRegression.fitIntercept is set to true, then the last element returned corresponds to the intercept.

    See also

    LinearRegression.solver

  25. val predictionCol: String
  26. val predictions: DataFrame
  27. val r2: Double

    Returns R2, the coefficient of determination.

    Returns R2, the coefficient of determination. Reference: Wikipedia coefficient of determination

    Annotations
    @Since( "1.5.0" )
    Note

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

  28. val r2adj: Double

    Returns Adjusted R2, the adjusted coefficient of determination.

    Returns Adjusted R2, the adjusted coefficient of determination. Reference: Wikipedia coefficient of determination

    Annotations
    @Since( "2.3.0" )
    Note

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

  29. lazy val residuals: DataFrame

    Residuals (label - predicted value)

    Residuals (label - predicted value)

    Annotations
    @Since( "1.5.0" ) @transient()
  30. 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.

    Annotations
    @Since( "1.5.0" )
    Note

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

  31. final def synchronized[T0](arg0: ⇒ T0): T0
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  32. lazy val tValues: Array[Double]

    T-statistic of estimated coefficients and intercept.

    T-statistic of estimated coefficients and intercept. This value is only available when using the "normal" solver.

    If LinearRegression.fitIntercept is set to true, then the last element returned corresponds to the intercept.

    See also

    LinearRegression.solver

  33. def toString(): String
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  34. final def wait(): Unit
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  36. final def wait(arg0: Long): Unit
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