Class

org.apache.spark.ml.regression

GeneralizedLinearRegressionTrainingSummary

Related Doc: package regression

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class GeneralizedLinearRegressionTrainingSummary extends GeneralizedLinearRegressionSummary with Serializable

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

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@Since( "2.0.0" ) @Experimental()
Source
GeneralizedLinearRegression.scala
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GeneralizedLinearRegressionSummary, Serializable, Serializable, AnyRef, Any
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  2. GeneralizedLinearRegressionSummary
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  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. lazy val aic: Double

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    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

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  6. def clone(): AnyRef

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    @throws( ... )
  7. lazy val coefficientStandardErrors: Array[Double]

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    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

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    Degrees of freedom.

    Degrees of freedom.

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

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    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

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    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

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  12. def equals(arg0: Any): Boolean

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  13. def finalize(): Unit

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

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  15. def hashCode(): Int

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  16. final def isInstanceOf[T0]: Boolean

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  17. val model: GeneralizedLinearRegressionModel

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    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

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  19. final def notify(): Unit

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  20. final def notifyAll(): Unit

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  21. lazy val nullDeviance: Double

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    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

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    Number of instances in DataFrame predictions.

    Number of instances in DataFrame predictions.

    Definition Classes
    GeneralizedLinearRegressionSummary
    Annotations
    @Since( "2.2.0" )
  23. val numIterations: Int

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    number of iterations

    number of iterations

    Annotations
    @Since( "2.0.0" )
  24. lazy val pValues: Array[Double]

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    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

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    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

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    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

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    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

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    The residual degrees of freedom.

    The residual degrees of freedom.

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

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    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

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    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

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    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

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    the solver algorithm used for model training

    the solver algorithm used for model training

    Annotations
    @Since( "2.0.0" )
  33. final def synchronized[T0](arg0: ⇒ T0): T0

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  34. lazy val tValues: Array[Double]

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    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

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

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