org.apache.spark.mllib.classification

Class LogisticRegressionWithSGD

Object

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

org.apache.spark.mllib.classification.LogisticRegressionWithSGD

All Implemented Interfaces:

java.io.Serializable, Logging

public class LogisticRegressionWithSGD
extends GeneralizedLinearAlgorithm<LogisticRegressionModel>
implements scala.Serializable

Train a classification model for Binary Logistic Regression using Stochastic Gradient Descent. By default L2 regularization is used, which can be changed via LogisticRegressionWithSGD.optimizer.

Using LogisticRegressionWithLBFGS is recommended over this.

See Also:

Serialized Form

Note:

Labels used in Logistic Regression should be {0, 1, ..., k - 1} for k classes multi-label classification problem.

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<LogisticRegressionModel>

Returns:

(undocumented)