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org.apache.spark.mllib.classification

LogisticRegressionWithLBFGS

class LogisticRegressionWithLBFGS extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable

Train a classification model for Multinomial/Binary Logistic Regression using Limited-memory BFGS. Standard feature scaling and L2 regularization are used by default.

Earlier implementations of LogisticRegressionWithLBFGS applies a regularization penalty to all elements including the intercept. If this is called with one of standard updaters (L1Updater, or SquaredL2Updater) this is translated into a call to ml.LogisticRegression, otherwise this will use the existing mllib GeneralizedLinearAlgorithm trainer, resulting in a regularization penalty to the intercept.

Annotations
@Since( "1.1.0" )
Source
LogisticRegression.scala
Note

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

Linear Supertypes
GeneralizedLinearAlgorithm[LogisticRegressionModel], Serializable, Serializable, Logging, AnyRef, Any
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Inherited
  1. LogisticRegressionWithLBFGS
  2. GeneralizedLinearAlgorithm
  3. Serializable
  4. Serializable
  5. Logging
  6. AnyRef
  7. Any
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Visibility
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Instance Constructors

  1. new LogisticRegressionWithLBFGS()

Value Members

  1. def getNumFeatures: Int

    The dimension of training features.

    The dimension of training features.

    Definition Classes
    GeneralizedLinearAlgorithm
    Annotations
    @Since( "1.4.0" )
  2. def isAddIntercept: Boolean

    Get if the algorithm uses addIntercept

    Get if the algorithm uses addIntercept

    Definition Classes
    GeneralizedLinearAlgorithm
    Annotations
    @Since( "1.4.0" )
  3. val optimizer: LBFGS

    The optimizer to solve the problem.

    The optimizer to solve the problem.

    Definition Classes
    LogisticRegressionWithLBFGS → GeneralizedLinearAlgorithm
    Annotations
    @Since( "1.1.0" )
  4. def run(input: RDD[LabeledPoint], initialWeights: Vector): LogisticRegressionModel

    Run Logistic Regression with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.

    Run Logistic Regression with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.

    If a known updater is used calls the ml implementation, to avoid applying a regularization penalty to the intercept, otherwise defaults to the mllib implementation. If more than two classes or feature scaling is disabled, always uses mllib implementation. Uses user provided weights.

    In the ml LogisticRegression implementation, the number of corrections used in the LBFGS update can not be configured. So optimizer.setNumCorrections() will have no effect if we fall into that route.

    Definition Classes
    LogisticRegressionWithLBFGS → GeneralizedLinearAlgorithm
  5. def run(input: RDD[LabeledPoint]): LogisticRegressionModel

    Run Logistic Regression with the configured parameters on an input RDD of LabeledPoint entries.

    Run Logistic Regression with the configured parameters on an input RDD of LabeledPoint entries.

    If a known updater is used calls the ml implementation, to avoid applying a regularization penalty to the intercept, otherwise defaults to the mllib implementation. If more than two classes or feature scaling is disabled, always uses mllib implementation. If using ml implementation, uses ml code to generate initial weights.

    Definition Classes
    LogisticRegressionWithLBFGS → GeneralizedLinearAlgorithm
  6. def setIntercept(addIntercept: Boolean): LogisticRegressionWithLBFGS.this.type

    Set if the algorithm should add an intercept.

    Set if the algorithm should add an intercept. Default false. We set the default to false because adding the intercept will cause memory allocation.

    Definition Classes
    GeneralizedLinearAlgorithm
    Annotations
    @Since( "0.8.0" )
  7. def setNumClasses(numClasses: Int): LogisticRegressionWithLBFGS.this.type

    Set the number of possible outcomes for k classes classification problem in Multinomial Logistic Regression.

    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.

    Annotations
    @Since( "1.3.0" )
  8. def setValidateData(validateData: Boolean): LogisticRegressionWithLBFGS.this.type

    Set if the algorithm should validate data before training.

    Set if the algorithm should validate data before training. Default true.

    Definition Classes
    GeneralizedLinearAlgorithm
    Annotations
    @Since( "0.8.0" )