org.apache.spark.mllib.regression

RidgeRegressionWithSGD

class RidgeRegressionWithSGD extends GeneralizedLinearAlgorithm[RidgeRegressionModel] with Serializable

Train a regression model with L2-regularization using Stochastic Gradient Descent. This solves the l1-regularized least squares regression formulation f(weights) = 1/n ||A weights-y||2 + regParam/2 ||weights||2 Here the data matrix has n rows, and the input RDD holds the set of rows of A, each with its corresponding right hand side label y. See also the documentation for the precise formulation.

Linear Supertypes
GeneralizedLinearAlgorithm[RidgeRegressionModel], Serializable, Serializable, Logging, AnyRef, Any
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  1. RidgeRegressionWithSGD
  2. GeneralizedLinearAlgorithm
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Instance Constructors

  1. new RidgeRegressionWithSGD()

    Construct a RidgeRegression object with default parameters: {stepSize: 1.0, numIterations: 100, regParam: 0.01, miniBatchFraction: 1.0}.

Value Members

  1. final def !=(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

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    AnyRef → Any
  4. var addIntercept: Boolean

    Whether to add intercept (default: false).

    Whether to add intercept (default: false).

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  5. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  6. def clone(): AnyRef

    Attributes
    protected[java.lang]
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    @throws( ... )
  7. def createModel(weights: Vector, intercept: Double): RidgeRegressionModel

    Create a model given the weights and intercept

    Create a model given the weights and intercept

    Attributes
    protected
    Definition Classes
    RidgeRegressionWithSGDGeneralizedLinearAlgorithm
  8. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  10. def finalize(): Unit

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

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  12. def hashCode(): Int

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  13. final def isInstanceOf[T0]: Boolean

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    Any
  14. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging
  15. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging
  16. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  17. def logDebug(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  18. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    protected
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    Logging
  19. def logError(msg: ⇒ String): Unit

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    protected
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    Logging
  20. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    protected
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    Logging
  21. def logInfo(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  22. def logName: String

    Attributes
    protected
    Definition Classes
    Logging
  23. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    protected
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    Logging
  24. def logTrace(msg: ⇒ String): Unit

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    Logging
  25. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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  26. def logWarning(msg: ⇒ String): Unit

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  27. final def ne(arg0: AnyRef): Boolean

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  28. final def notify(): Unit

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  29. final def notifyAll(): Unit

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    AnyRef
  30. val optimizer: GradientDescent

    The optimizer to solve the problem.

    The optimizer to solve the problem.

    Definition Classes
    RidgeRegressionWithSGDGeneralizedLinearAlgorithm
  31. def run(input: RDD[LabeledPoint], initialWeights: Vector): RidgeRegressionModel

    Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.

    Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.

    Definition Classes
    GeneralizedLinearAlgorithm
  32. def run(input: RDD[LabeledPoint]): RidgeRegressionModel

    Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.

    Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.

    Definition Classes
    GeneralizedLinearAlgorithm
  33. def setIntercept(addIntercept: Boolean): RidgeRegressionWithSGD.this.type

    Set if the algorithm should add an intercept.

    Set if the algorithm should add an intercept. Default false. We set the default to false because adding the intercept will cause memory allocation.

    Definition Classes
    GeneralizedLinearAlgorithm
  34. def setValidateData(validateData: Boolean): RidgeRegressionWithSGD.this.type

    Set if the algorithm should validate data before training.

    Set if the algorithm should validate data before training. Default true.

    Definition Classes
    GeneralizedLinearAlgorithm
  35. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  36. def toString(): String

    Definition Classes
    AnyRef → Any
  37. var validateData: Boolean

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  38. val validators: Seq[(RDD[LabeledPoint]) ⇒ Boolean]

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  39. final def wait(): Unit

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

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

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