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org.apache.spark.ml.regression

GeneralizedLinearRegressionTrainingSummary

class GeneralizedLinearRegressionTrainingSummary extends GeneralizedLinearRegressionSummary with Serializable

:: Experimental :: Summary of GeneralizedLinearRegression fitting and model.

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@Since( "2.0.0" ) @Experimental()
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GeneralizedLinearRegressionSummary, Serializable, Serializable, AnyRef, Any
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  1. final def !=(arg0: Any): Boolean
    Definition Classes
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  2. final def ##(): Int
    Definition Classes
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  3. final def ==(arg0: Any): Boolean
    Definition Classes
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  4. lazy val aic: Double

    Akaike Information Criterion (AIC) for the fitted model.

    Akaike Information Criterion (AIC) for the fitted model.

    Definition Classes
    GeneralizedLinearRegressionSummary
    Annotations
    @Since( "2.0.0" )
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
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    @native() @throws( ... )
  7. 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 the underlying WeightedLeastSquares using the "normal" solver.

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

    Annotations
    @Since( "2.0.0" )
  8. lazy val degreesOfFreedom: Long

    Degrees of freedom.

    Degrees of freedom.

    Definition Classes
    GeneralizedLinearRegressionSummary
    Annotations
    @Since( "2.0.0" )
  9. lazy val deviance: Double

    The deviance for the fitted model.

    The deviance for the fitted model.

    Definition Classes
    GeneralizedLinearRegressionSummary
    Annotations
    @Since( "2.0.0" )
  10. lazy val dispersion: Double

    The dispersion of the fitted model.

    The dispersion of the fitted model. It is taken as 1.0 for the "binomial" and "poisson" families, and otherwise estimated by the residual Pearson's Chi-Squared statistic (which is defined as sum of the squares of the Pearson residuals) divided by the residual degrees of freedom.

    Definition Classes
    GeneralizedLinearRegressionSummary
    Annotations
    @Since( "2.0.0" )
  11. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean
    Definition Classes
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  13. def finalize(): Unit
    Attributes
    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  14. final def getClass(): Class[_]
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    @native()
  15. def hashCode(): Int
    Definition Classes
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    @native()
  16. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  17. val model: GeneralizedLinearRegressionModel

    Private copy of model to ensure Params are not modified outside this class.

    Private copy of model to ensure Params are not modified outside this class. Coefficients is not a deep copy, but that is acceptable.

    Attributes
    protected
    Definition Classes
    GeneralizedLinearRegressionSummary
    Note

    predictionCol must be set correctly before the value of model is set, and model must be set before predictions is set!

  18. final def ne(arg0: AnyRef): Boolean
    Definition Classes
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  19. final def notify(): Unit
    Definition Classes
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    @native()
  20. final def notifyAll(): Unit
    Definition Classes
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    @native()
  21. lazy val nullDeviance: Double

    The deviance for the null model.

    The deviance for the null model.

    Definition Classes
    GeneralizedLinearRegressionSummary
    Annotations
    @Since( "2.0.0" )
  22. lazy val numInstances: Long

    Number of instances in DataFrame predictions.

    Number of instances in DataFrame predictions.

    Definition Classes
    GeneralizedLinearRegressionSummary
    Annotations
    @Since( "2.2.0" )
  23. val numIterations: Int
    Annotations
    @Since( "2.0.0" )
  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 the underlying WeightedLeastSquares using the "normal" solver.

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

    Annotations
    @Since( "2.0.0" )
  25. val predictionCol: String

    Field in "predictions" which gives the predicted value of each instance.

    Field in "predictions" which gives the predicted value of each instance. This is set to a new column name if the original model's predictionCol is not set.

    Definition Classes
    GeneralizedLinearRegressionSummary
    Annotations
    @Since( "2.0.0" )
  26. val predictions: DataFrame

    Predictions output by the model's transform method.

    Predictions output by the model's transform method.

    Definition Classes
    GeneralizedLinearRegressionSummary
    Annotations
    @Since( "2.0.0" )
  27. lazy val rank: Long

    The numeric rank of the fitted linear model.

    The numeric rank of the fitted linear model.

    Definition Classes
    GeneralizedLinearRegressionSummary
    Annotations
    @Since( "2.0.0" )
  28. lazy val residualDegreeOfFreedom: Long

    The residual degrees of freedom.

    The residual degrees of freedom.

    Definition Classes
    GeneralizedLinearRegressionSummary
    Annotations
    @Since( "2.0.0" )
  29. lazy val residualDegreeOfFreedomNull: Long

    The residual degrees of freedom for the null model.

    The residual degrees of freedom for the null model.

    Definition Classes
    GeneralizedLinearRegressionSummary
    Annotations
    @Since( "2.0.0" )
  30. def residuals(residualsType: String): DataFrame

    Get the residuals of the fitted model by type.

    Get the residuals of the fitted model by type.

    residualsType

    The type of residuals which should be returned. Supported options: deviance, pearson, working and response.

    Definition Classes
    GeneralizedLinearRegressionSummary
    Annotations
    @Since( "2.0.0" )
  31. def residuals(): DataFrame

    Get the default residuals (deviance residuals) of the fitted model.

    Get the default residuals (deviance residuals) of the fitted model.

    Definition Classes
    GeneralizedLinearRegressionSummary
    Annotations
    @Since( "2.0.0" )
  32. val solver: String
    Annotations
    @Since( "2.0.0" )
  33. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  34. 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 the underlying WeightedLeastSquares using the "normal" solver.

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

    Annotations
    @Since( "2.0.0" )
  35. def toString(): String
    Definition Classes
    GeneralizedLinearRegressionTrainingSummary → AnyRef → Any
  36. final def wait(): Unit
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    @throws( ... )
  37. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  38. final def wait(arg0: Long): Unit
    Definition Classes
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    @native() @throws( ... )

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