 # GeneralizedLinearRegressionTrainingSummary

### Related Doc: package regression

#### 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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### 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.

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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protected[java.lang]
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@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

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

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13. #### def finalize(): Unit

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protected[java.lang]
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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

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

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

number of iterations

number of iterations

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

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

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

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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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