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.

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

Value Members

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

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    AnyRef
  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. var addIntercept: Boolean

    Whether to add intercept (default: false).

    Whether to add intercept (default: false).

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

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  8. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  9. 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
  10. final def eq(arg0: AnyRef): Boolean

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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  13. final def getClass(): Class[_]

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging
  17. def log: Logger

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    Logging
  18. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  19. def logDebug(msg: ⇒ String): Unit

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  20. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  21. def logError(msg: ⇒ String): Unit

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  22. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  23. def logInfo(msg: ⇒ String): Unit

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  24. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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  25. def logTrace(msg: ⇒ String): Unit

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

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

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

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  29. final def notify(): Unit

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  30. final def notifyAll(): Unit

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  31. val optimizer: GradientDescent

    The optimizer to solve the problem.

    The optimizer to solve the problem.

    Definition Classes
    RidgeRegressionWithSGDGeneralizedLinearAlgorithm
  32. 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
  33. 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
  34. 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
  35. 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
  36. final def synchronized[T0](arg0: ⇒ T0): T0

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  37. def toString(): String

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  38. var validateData: Boolean

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

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

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

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  42. final def wait(arg0: Long): Unit

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Inherited from Serializable

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