# 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) = 1/2n ||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.

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: 0.01, 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).

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

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Logging
17. #### def log: Logger

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

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24. #### def logName: String

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

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26. #### def logTrace(msg: ⇒ String): Unit

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

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28. #### def logWarning(msg: ⇒ String): Unit

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29. #### final def ne(arg0: AnyRef): Boolean

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30. #### final def notify(): Unit

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31. #### final def notifyAll(): Unit

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32. #### var numFeatures: Int

The dimension of training features.

The dimension of training features.

Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm
33. #### var numOfLinearPredictor: Int

In `GeneralizedLinearModel`, only single linear predictor is allowed for both weights and intercept.

In `GeneralizedLinearModel`, only single linear predictor is allowed for both weights and intercept. However, for multinomial logistic regression, with K possible outcomes, we are training K-1 independent binary logistic regression models which requires K-1 sets of linear predictor.

As a result, the workaround here is if more than two sets of linear predictors are needed, we construct bigger `weights` vector which can hold both weights and intercepts. If the intercepts are added, the dimension of `weights` will be (numOfLinearPredictor) * (numFeatures + 1) . If the intercepts are not added, the dimension of `weights` will be (numOfLinearPredictor) * numFeatures.

Thus, the intercepts will be encapsulated into weights, and we leave the value of intercept in GeneralizedLinearModel as zero.

Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm

The optimizer to solve the problem.

The optimizer to solve the problem.

Definition Classes
RidgeRegressionWithSGDGeneralizedLinearAlgorithm
35. #### 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
36. #### 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
37. #### 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
38. #### 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
39. #### final def synchronized[T0](arg0: ⇒ T0): T0

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AnyRef
40. #### def toString(): String

Definition Classes
AnyRef → Any
41. #### var validateData: Boolean

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

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

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

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45. #### final def wait(arg0: Long): Unit

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