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. 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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  1. LogisticRegressionWithLBFGS
  2. GeneralizedLinearAlgorithm
  3. Serializable
  4. Serializable
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Instance Constructors

  1. new LogisticRegressionWithLBFGS()

Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

    Definition Classes
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  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any
  6. var addIntercept: Boolean

    Whether to add intercept (default: false).

    Whether to add intercept (default: false).

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  8. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. def createModel(weights: Vector, intercept: Double): LogisticRegressionModel

    Create a model given the weights and intercept

    Create a model given the weights and intercept

    Attributes
    protected
    Definition Classes
    LogisticRegressionWithLBFGSGeneralizedLinearAlgorithm
  10. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  11. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  12. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  13. final def getClass(): Class[_]

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging
  17. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging
  18. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  19. def logDebug(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  20. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    protected
    Definition Classes
    Logging
  21. def logError(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  22. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    protected
    Definition Classes
    Logging
  23. def logInfo(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  24. def logName: String

    Attributes
    protected
    Definition Classes
    Logging
  25. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    protected
    Definition Classes
    Logging
  26. def logTrace(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  27. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    protected
    Definition Classes
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  28. def logWarning(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  29. final def ne(arg0: AnyRef): Boolean

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  30. final def notify(): Unit

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  31. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  32. var numFeatures: Int

    The dimension of training features.

    The dimension of training features.

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  33. var numOfLinearPredictor: Int

    In GeneralizedLinearModel, only single linear predictor is allowed for both weights and intercept.

    In GeneralizedLinearModel, only single linear predictor is allowed for both weights and intercept. However, for multinomial logistic regression, with K possible outcomes, we are training K-1 independent binary logistic regression models which requires K-1 sets of linear predictor.

    As a result, the workaround here is if more than two sets of linear predictors are needed, we construct bigger weights vector which can hold both weights and intercepts. If the intercepts are added, the dimension of weights will be (numOfLinearPredictor) * (numFeatures + 1) . If the intercepts are not added, the dimension of weights will be (numOfLinearPredictor) * numFeatures.

    Thus, the intercepts will be encapsulated into weights, and we leave the value of intercept in GeneralizedLinearModel as zero.

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  34. val optimizer: LBFGS

    The optimizer to solve the problem.

    The optimizer to solve the problem.

    Definition Classes
    LogisticRegressionWithLBFGSGeneralizedLinearAlgorithm
  35. def run(input: RDD[LabeledPoint], initialWeights: Vector): LogisticRegressionModel

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

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

    Definition Classes
    GeneralizedLinearAlgorithm
  36. def run(input: RDD[LabeledPoint]): LogisticRegressionModel

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

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

    Definition Classes
    GeneralizedLinearAlgorithm
  37. 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
  38. def setNumClasses(numClasses: Int): LogisticRegressionWithLBFGS.this.type

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

    :: 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.

    Annotations
    @Experimental()
  39. 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
  40. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  41. def toString(): String

    Definition Classes
    AnyRef → Any
  42. var validateData: Boolean

    Attributes
    protected
    Definition Classes
    GeneralizedLinearAlgorithm
  43. val validators: List[(RDD[LabeledPoint]) ⇒ Boolean]

    Attributes
    protected
    Definition Classes
    LogisticRegressionWithLBFGSGeneralizedLinearAlgorithm
  44. final def wait(): Unit

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    @throws( ... )
  45. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  46. final def wait(arg0: Long): Unit

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