Package org.apache.spark.mllib.regression

Class LinearRegressionWithSGD

Object
org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm<LinearRegressionModel>
org.apache.spark.mllib.regression.LinearRegressionWithSGD
All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging
public class LinearRegressionWithSGD extends GeneralizedLinearAlgorithm<LinearRegressionModel> implements 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.
See Also:

  • Nested Class Summary

    Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

    org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter

  • Method Summary

    Modifier and Type
    Method
    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 java.lang.Object

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

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

    initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext