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c

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 l2-regularized least squares regression formulation f(weights) = 1/2n ||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.

Annotations
@Since( "0.8.0" )
Source
RidgeRegression.scala
Linear Supertypes
GeneralizedLinearAlgorithm[RidgeRegressionModel], Serializable, Serializable, Logging, AnyRef, Any
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Inherited
  1. RidgeRegressionWithSGD
  2. GeneralizedLinearAlgorithm
  3. Serializable
  4. Serializable
  5. Logging
  6. AnyRef
  7. Any
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Visibility
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Value Members

  1. def getNumFeatures: Int

    The dimension of training features.

    The dimension of training features.

    Definition Classes
    GeneralizedLinearAlgorithm
    Annotations
    @Since( "1.4.0" )
  2. def isAddIntercept: Boolean

    Get if the algorithm uses addIntercept

    Get if the algorithm uses addIntercept

    Definition Classes
    GeneralizedLinearAlgorithm
    Annotations
    @Since( "1.4.0" )
  3. val optimizer: GradientDescent

    The optimizer to solve the problem.

    The optimizer to solve the problem.

    Definition Classes
    RidgeRegressionWithSGDGeneralizedLinearAlgorithm
    Annotations
    @Since( "0.8.0" )
  4. 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
    Annotations
    @Since( "1.0.0" )
  5. 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
    Annotations
    @Since( "0.8.0" )
  6. 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
    Annotations
    @Since( "0.8.0" )
  7. 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
    Annotations
    @Since( "0.8.0" )