# GeneralizedLinearRegressionSummary

### Related Doc: package regression

#### class GeneralizedLinearRegressionSummary extends Serializable

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

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@Since( "2.0.0" ) @Experimental()
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GeneralizedLinearRegression.scala
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### Value Members

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

Akaike Information Criterion (AIC) for the fitted model.

Akaike Information Criterion (AIC) for the fitted model.

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@Since( "2.0.0" )
5. #### final def asInstanceOf[T0]: T0

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6. #### def clone(): AnyRef

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7. #### lazy val degreesOfFreedom: Long

Degrees of freedom.

Degrees of freedom.

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@Since( "2.0.0" )
8. #### lazy val deviance: Double

The deviance for the fitted model.

The deviance for the fitted model.

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@Since( "2.0.0" )
9. #### 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.

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@Since( "2.0.0" )
10. #### final def eq(arg0: AnyRef): Boolean

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11. #### def equals(arg0: Any): Boolean

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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 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.

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

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17. #### final def ne(arg0: AnyRef): Boolean

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18. #### final def notify(): Unit

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19. #### final def notifyAll(): Unit

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20. #### lazy val nullDeviance: Double

The deviance for the null model.

The deviance for the null model.

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@Since( "2.0.0" )
21. #### 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.

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@Since( "2.0.0" )
22. #### val predictions: DataFrame

Predictions output by the model's `transform` method.

Predictions output by the model's `transform` method.

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@Since( "2.0.0" )
23. #### lazy val rank: Long

The numeric rank of the fitted linear model.

The numeric rank of the fitted linear model.

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@Since( "2.0.0" )
24. #### lazy val residualDegreeOfFreedom: Long

The residual degrees of freedom.

The residual degrees of freedom.

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@Since( "2.0.0" )
25. #### lazy val residualDegreeOfFreedomNull: Long

The residual degrees of freedom for the null model.

The residual degrees of freedom for the null model.

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@Since( "2.0.0" )
26. #### 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.

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@Since( "2.0.0" )
27. #### def residuals(): DataFrame

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

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

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

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29. #### def toString(): String

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30. #### final def wait(): Unit

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