RidgeRegressionWithSGD

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def train(input: RDD[LabeledPoint], numIterations: Int): RidgeRegressionModel

Train a RidgeRegression model given an RDD of (label, features) pairs.
Train a RidgeRegression model given an RDD of (label, features) pairs. We run a fixed number of iterations of gradient descent using a step size of 1.0. We use the entire data set to compute the true gradient in each iteration.
input
RDD of (label, array of features) pairs.
numIterations
Number of iterations of gradient descent to run.
returns
a RidgeRegressionModel which has the weights and offset from training.

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@Since( "0.8.0" )
def train(input: RDD[LabeledPoint], numIterations: Int, stepSize: Double, regParam: Double): RidgeRegressionModel

Train a RidgeRegression model given an RDD of (label, features) pairs.
Train a RidgeRegression model given an RDD of (label, features) pairs. We run a fixed number of iterations of gradient descent using the specified step size. We use the entire data set to compute the true gradient in each iteration.
input
RDD of (label, array of features) pairs.
numIterations
Number of iterations of gradient descent to run.
stepSize
Step size to be used for each iteration of Gradient Descent.
regParam
Regularization parameter.
returns
a RidgeRegressionModel which has the weights and offset from training.

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@Since( "0.8.0" )
def train(input: RDD[LabeledPoint], numIterations: Int, stepSize: Double, regParam: Double, miniBatchFraction: Double): RidgeRegressionModel

Train a RidgeRegression model given an RDD of (label, features) pairs.
Train a RidgeRegression model given an RDD of (label, features) pairs. We run a fixed number of iterations of gradient descent using the specified step size. Each iteration uses miniBatchFraction fraction of the data to calculate a stochastic gradient.
input
RDD of (label, array of features) pairs.
numIterations
Number of iterations of gradient descent to run.
stepSize
Step size to be used for each iteration of gradient descent.
regParam
Regularization parameter.
miniBatchFraction
Fraction of data to be used per iteration.

Annotations
@Since( "0.8.0" )
def train(input: RDD[LabeledPoint], numIterations: Int, stepSize: Double, regParam: Double, miniBatchFraction: Double, initialWeights: Vector): RidgeRegressionModel

Train a RidgeRegression model given an RDD of (label, features) pairs.
Train a RidgeRegression model given an RDD of (label, features) pairs. We run a fixed number of iterations of gradient descent using the specified step size. Each iteration uses miniBatchFraction fraction of the data to calculate a stochastic gradient. The weights used in gradient descent are initialized using the initial weights provided.
input
RDD of (label, array of features) pairs.
numIterations
Number of iterations of gradient descent to run.
stepSize
Step size to be used for each iteration of gradient descent.
regParam
Regularization parameter.
miniBatchFraction
Fraction of data to be used per iteration.
initialWeights
Initial set of weights to be used. Array should be equal in size to the number of features in the data.

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@Since( "1.0.0" )
final def wait(): Unit

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Related Docs: class RidgeRegressionWithSGD | package regression

object RidgeRegressionWithSGD extends Serializable

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final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

def train(input: RDD[LabeledPoint], numIterations: Int): RidgeRegressionModel

def train(input: RDD[LabeledPoint], numIterations: Int, stepSize: Double, regParam: Double): RidgeRegressionModel

def train(input: RDD[LabeledPoint], numIterations: Int, stepSize: Double, regParam: Double, miniBatchFraction: Double): RidgeRegressionModel

def train(input: RDD[LabeledPoint], numIterations: Int, stepSize: Double, regParam: Double, miniBatchFraction: Double, initialWeights: Vector): RidgeRegressionModel

final def wait(): Unit

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

final def wait(arg0: Long): Unit

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