Package org.apache.spark.ml.regression

Class GeneralizedLinearRegression.Gaussian$

Object
org.apache.spark.ml.regression.GeneralizedLinearRegression.Gaussian$
All Implemented Interfaces:
Serializable
Enclosing class:
GeneralizedLinearRegression
public static class GeneralizedLinearRegression.Gaussian$ extends Object
Gaussian exponential family distribution. The default link for the Gaussian family is the identity link.
See Also:

  • Field Summary

    Fields
    Modifier and Type
    Field
    Description
    static final GeneralizedLinearRegression.Gaussian$
    MODULE$
    Static reference to the singleton instance of this Scala object.

  • Constructor Summary

    Constructors
    Constructor
    Description
    Gaussian$()
     

  • Method Summary

    Modifier and Type
    Method
    Description
    double
    aic(RDD<scala.Tuple3<Object,Object,Object>> predictions, double deviance, double numInstances, double weightSum)
    Akaike Information Criterion (AIC) value of the family for a given dataset.
    org.apache.spark.ml.regression.GeneralizedLinearRegression.Link
    defaultLink()
    The default link instance of this family.
    double
    deviance(double y, double mu, double weight)
    Deviance of (y, mu) pair.
    double
    initialize(double y, double weight)
    Initialize the starting value for mu.
    String
    name()
     
    double
    project(double mu)
    Trim the fitted value so that it will be in valid range.
    double
    variance(double mu)
    The variance of the endogenous variable's mean, given the value mu.
    double
    variancePower()
     

    Methods inherited from class java.lang.Object

    equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

  • Field Details

  • Constructor Details

    • Gaussian$

      public Gaussian$()

  • Method Details

    • name

      public String name()

    • defaultLink

      public org.apache.spark.ml.regression.GeneralizedLinearRegression.Link defaultLink()
      The default link instance of this family.

    • initialize

      public double initialize(double y, double weight)
      Initialize the starting value for mu.

    • variance

      public double variance(double mu)
      The variance of the endogenous variable's mean, given the value mu.

    • deviance

      public double deviance(double y, double mu, double weight)
      Deviance of (y, mu) pair.

    • aic

      public double aic(RDD<scala.Tuple3<Object,Object,Object>> predictions, double deviance, double numInstances, double weightSum)
      Akaike Information Criterion (AIC) value of the family for a given dataset.

      Parameters:
      predictions - an RDD of (y, mu, weight) of instances in evaluation dataset
      deviance - the deviance for the fitted model in evaluation dataset
      numInstances - number of instances in evaluation dataset
      weightSum - weights sum of instances in evaluation dataset
      Returns:
      (undocumented)

    • project

      public double project(double mu)
      Trim the fitted value so that it will be in valid range.

    • variancePower

      public double variancePower()