org.apache.spark.mllib.regression

Class LassoWithSGD

Object

org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm<LassoModel>

org.apache.spark.mllib.regression.LassoWithSGD

All Implemented Interfaces:

java.io.Serializable, Logging

public class LassoWithSGD
extends GeneralizedLinearAlgorithm<LassoModel>
implements scala.Serializable

Train a regression model with L1-regularization using Stochastic Gradient Descent. This solves the l1-regularized least squares regression formulation f(weights) = 1/2n ||A weights-y||^2^ + regParam ||weights||_1 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.

See Also:

Serialized Form

Method Summary

Modifier and Type	Method and Description
GradientDescent	optimizer() The optimizer to solve the problem.

Methods inherited from class org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm

getNumFeatures, isAddIntercept, run, run, setIntercept, setValidateData

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.internal.Logging

initializeForcefully, initializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Method Detail

optimizer

public GradientDescent optimizer()

Description copied from class: GeneralizedLinearAlgorithm

The optimizer to solve the problem.

Specified by:

optimizer in class GeneralizedLinearAlgorithm<LassoModel>

Returns:

(undocumented)