# 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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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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Whether to add intercept (default: false).

Whether to add intercept (default: false).

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

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8. #### def clone(): AnyRef

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9. #### def createModel(weights: Vector, intercept: Double): RidgeRegressionModel

Create a model given the weights and intercept

Create a model given the weights and intercept

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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

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Logging
17. #### def log: Logger

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

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

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

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Logging
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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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]

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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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