GeneralizedLinearRegressionTrainingSummary

class GeneralizedLinearRegressionTrainingSummary extends GeneralizedLinearRegressionSummary with Serializable

Summary of GeneralizedLinearRegression fitting and model.

Annotations: @Since("2.0.0")
Source: GeneralizedLinearRegression.scala

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GeneralizedLinearRegressionSummary, Serializable, AnyRef, Any

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GeneralizedLinearRegressionTrainingSummary
GeneralizedLinearRegressionSummary
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final def !=(arg0: Any): Boolean
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final def ##: Int
Definition Classes
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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[lang]
Definition Classes
AnyRef
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@throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
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
Definition Classes
AnyRef
def equals(arg0: AnyRef): Boolean
Definition Classes
AnyRef → Any
final def getClass(): Class[_ <: AnyRef]
Definition Classes
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@IntrinsicCandidate() @native()
def hashCode(): Int
Definition Classes
AnyRef → Any
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@IntrinsicCandidate() @native()
final def isInstanceOf[T0]: Boolean
Definition Classes
Any
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
AnyRef
final def notify(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
final def notifyAll(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
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
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
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
GeneralizedLinearRegressionTrainingSummary → AnyRef → Any
final def wait(arg0: Long, arg1: Int): Unit
Definition Classes
AnyRef
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@throws(classOf[java.lang.InterruptedException])
final def wait(arg0: Long): Unit
Definition Classes
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@throws(classOf[java.lang.InterruptedException]) @native()
final def wait(): Unit
Definition Classes
AnyRef
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@throws(classOf[java.lang.InterruptedException])

Inherited from GeneralizedLinearRegressionSummary

Value Members

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")
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")
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!
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 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")

Inherited from AnyRef

Value Members

final def !=(arg0: Any): Boolean
Definition Classes
AnyRef → Any
final def ##: Int
Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean
Definition Classes
AnyRef → Any
def clone(): AnyRef
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
final def eq(arg0: AnyRef): Boolean
Definition Classes
AnyRef
def equals(arg0: AnyRef): Boolean
Definition Classes
AnyRef → Any
final def getClass(): Class[_ <: AnyRef]
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @native()
def hashCode(): Int
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @native()
final def ne(arg0: AnyRef): Boolean
Definition Classes
AnyRef
final def notify(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
final def notifyAll(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
final def synchronized[T0](arg0: => T0): T0
Definition Classes
AnyRef
final def wait(arg0: Long, arg1: Int): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
final def wait(arg0: Long): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException]) @native()
final def wait(): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
def finalize(): Unit
Attributes
protected[lang]
Definition Classes
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@throws(classOf[java.lang.Throwable]) @Deprecated
Deprecated
(Since version 9)

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Value Members

final def asInstanceOf[T0]: T0
Definition Classes
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final def isInstanceOf[T0]: Boolean
Definition Classes
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Members

final def !=(arg0: Any): Boolean
Definition Classes
AnyRef → Any
final def ##: Int
Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean
Definition Classes
AnyRef → Any
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[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
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
Definition Classes
AnyRef
def equals(arg0: AnyRef): Boolean
Definition Classes
AnyRef → Any
final def getClass(): Class[_ <: AnyRef]
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @native()
def hashCode(): Int
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @native()
final def isInstanceOf[T0]: Boolean
Definition Classes
Any
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
AnyRef
final def notify(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
final def notifyAll(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
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
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
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
GeneralizedLinearRegressionTrainingSummary → AnyRef → Any
final def wait(arg0: Long, arg1: Int): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
final def wait(arg0: Long): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException]) @native()
final def wait(): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
def finalize(): Unit
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.Throwable]) @Deprecated
Deprecated
(Since version 9)

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GeneralizedLinearRegressionTrainingSummary

class GeneralizedLinearRegressionTrainingSummary extends GeneralizedLinearRegressionSummary with Serializable

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GeneralizedLinearRegressionTrainingSummary

class GeneralizedLinearRegressionTrainingSummary extends GeneralizedLinearRegressionSummary with Serializable

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Inherited from GeneralizedLinearRegressionSummary

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Inherited from AnyRef

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Inherited from Any

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GeneralizedLinearRegressionTrainingSummary