GeneralizedLinearRegressionTrainingSummary

Value Members

final def !=(arg0: AnyRef): Boolean

Definition Classes
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final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: AnyRef): Boolean

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final def ==(arg0: Any): Boolean

Definition Classes
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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" )
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
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@throws( ... )
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" )
lazy val degreesOfFreedom: Long

Degrees of freedom.
Degrees of freedom.

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

The deviance for the fitted model.
The deviance for the fitted model.

Definition Classes
GeneralizedLinearRegressionSummary
Annotations
@Since( "2.0.0" )
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" )
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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AnyRef → Any
def finalize(): Unit

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

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def hashCode(): Int

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final def isInstanceOf[T0]: Boolean

Definition Classes
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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!
final def ne(arg0: AnyRef): Boolean

Definition Classes
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final def notify(): Unit

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final def notifyAll(): Unit

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lazy val nullDeviance: Double

The deviance for the null model.
The deviance for the null model.

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

Number of instances in DataFrame predictions.
Number of instances in DataFrame predictions.

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

number of iterations
number of iterations

Annotations
@Since( "2.0.0" )
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" )
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" )
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" )
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" )
lazy val residualDegreeOfFreedom: Long

The residual degrees of freedom.
The residual degrees of freedom.

Definition Classes
GeneralizedLinearRegressionSummary
Annotations
@Since( "2.0.0" )
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" )
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" )
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" )
val solver: String

the solver algorithm used for model training
the solver algorithm used for model training

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

Definition Classes
AnyRef
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" )
def toString(): String

Definition Classes
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final def wait(): Unit

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@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
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@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
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@throws( ... )

class GeneralizedLinearRegressionTrainingSummary extends GeneralizedLinearRegressionSummary with Serializable

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

lazy val aic: Double

final def asInstanceOf[T0]: T0

def clone(): AnyRef

lazy val coefficientStandardErrors: Array[Double]

lazy val degreesOfFreedom: Long

lazy val deviance: Double

lazy val dispersion: Double

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

val model: GeneralizedLinearRegressionModel

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

lazy val nullDeviance: Double

lazy val numInstances: Long

val numIterations: Int

lazy val pValues: Array[Double]

val predictionCol: String

val predictions: DataFrame

lazy val rank: Long

lazy val residualDegreeOfFreedom: Long

lazy val residualDegreeOfFreedomNull: Long

def residuals(residualsType: String): DataFrame

def residuals(): DataFrame

val solver: String

final def synchronized[T0](arg0: ⇒ T0): T0

lazy val tValues: Array[Double]

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from GeneralizedLinearRegressionSummary

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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