# LinearRegressionWithSGD

#### class LinearRegressionWithSGD extends GeneralizedLinearAlgorithm[LinearRegressionModel] with Serializable

Train a linear regression model with no regularization using Stochastic Gradient Descent. This solves the least squares regression formulation f(weights) = 1/n ||A weights-y||^2 (which is the mean squared error). 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[LinearRegressionModel], Serializable, Serializable, Logging, AnyRef, Any
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1. LinearRegressionWithSGD
2. GeneralizedLinearAlgorithm
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### Instance Constructors

1. #### new LinearRegressionWithSGD()

Construct a LinearRegression object with default parameters: {stepSize: 1.

Construct a LinearRegression object with default parameters: {stepSize: 1.0, numIterations: 100, 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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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): LinearRegressionModel

Create a model given the weights and intercept

Create a model given the weights and intercept

Attributes
protected[org.apache.spark.mllib]
Definition Classes
LinearRegressionWithSGDGeneralizedLinearAlgorithm
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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protected
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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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Logging
23. #### def logInfo(msg: ⇒ String): Unit

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Logging
24. #### def logName: String

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

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

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

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

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protected
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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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The optimizer to solve the problem.

The optimizer to solve the problem.

Definition Classes
LinearRegressionWithSGDGeneralizedLinearAlgorithm
33. #### def run(input: RDD[LabeledPoint], initialWeights: Vector): LinearRegressionModel

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
34. #### def run(input: RDD[LabeledPoint]): LinearRegressionModel

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
35. #### def setIntercept(addIntercept: Boolean): LinearRegressionWithSGD.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
36. #### def setValidateData(validateData: Boolean): LinearRegressionWithSGD.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
37. #### final def synchronized[T0](arg0: ⇒ T0): T0

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

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39. #### var validateData: Boolean

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

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

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

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

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