org.apache.spark.mllib.classification
Class LogisticRegressionWithLBFGS

Object
  org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm<LogisticRegressionModel>
      org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS

All Implemented Interfaces:

java.io.Serializable, Logging

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

Train a classification model for Multinomial/Binary Logistic Regression using Limited-memory BFGS. Standard feature scaling and L2 regularization are used by default. NOTE: Labels used in Logistic Regression should be {0, 1, ..., k - 1} for k classes multi-label classification problem.

See Also:

Serialized Form

Constructor Summary

LogisticRegressionWithLBFGS()

Method Summary

LBFGS	optimizer() The optimizer to solve the problem.
LogisticRegressionWithLBFGS	setNumClasses(int numClasses) :: Experimental :: Set the number of possible outcomes for k classes classification problem in Multinomial Logistic Regression.

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.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

LogisticRegressionWithLBFGS

public LogisticRegressionWithLBFGS()

Method Detail

optimizer

public LBFGS optimizer()

Description copied from class: GeneralizedLinearAlgorithm

The optimizer to solve the problem.

Specified by:

optimizer in class GeneralizedLinearAlgorithm<LogisticRegressionModel>

setNumClasses

public LogisticRegressionWithLBFGS setNumClasses(int numClasses)

:: Experimental :: Set the number of possible outcomes for k classes classification problem in Multinomial Logistic Regression. By default, it is binary logistic regression so k will be set to 2.

Parameters:

numClasses - (undocumented)

Returns:

(undocumented)