org.apache.spark.mllib.classification

LogisticRegressionWithSGD

class LogisticRegressionWithSGD extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable

Train a classification model for Logistic Regression using Stochastic Gradient Descent. By default L2 regularization is used, which can be changed via LogisticRegressionWithSGD.optimizer. NOTE: Labels used in Logistic Regression should be {0, 1}. Using LogisticRegressionWithLBFGS is recommended over this.

Linear Supertypes
GeneralizedLinearAlgorithm[LogisticRegressionModel], Serializable, Serializable, Logging, AnyRef, Any
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  1. LogisticRegressionWithSGD
  2. GeneralizedLinearAlgorithm
  3. Serializable
  4. Serializable
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Instance Constructors

  1. new LogisticRegressionWithSGD()

    Construct a LogisticRegression object with default parameters: {stepSize: 1.

    Construct a LogisticRegression object with default parameters: {stepSize: 1.0, numIterations: 100, regParm: 0.01, miniBatchFraction: 1.0}.

Value Members

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

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

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    Any
  3. final def ##(): Int

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    AnyRef → Any
  4. final def ==(arg0: AnyRef): Boolean

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    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]
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    @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
    LogisticRegressionWithSGDGeneralizedLinearAlgorithm
  10. final def eq(arg0: AnyRef): Boolean

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  11. def equals(arg0: Any): Boolean

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    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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    AnyRef → Any
  15. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  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

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

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

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

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    protected
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    Logging
  24. def logName: String

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

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

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  27. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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  28. def logWarning(msg: ⇒ String): Unit

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  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. val optimizer: GradientDescent

    The optimizer to solve the problem.

    The optimizer to solve the problem.

    Definition Classes
    LogisticRegressionWithSGDGeneralizedLinearAlgorithm
  33. 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
  34. 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
  35. def setIntercept(addIntercept: Boolean): LogisticRegressionWithSGD.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
  36. def setValidateData(validateData: Boolean): LogisticRegressionWithSGD.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
  37. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  38. def toString(): String

    Definition Classes
    AnyRef → Any
  39. var validateData: Boolean

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

    Attributes
    protected
    Definition Classes
    LogisticRegressionWithSGDGeneralizedLinearAlgorithm
  41. final def wait(): Unit

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

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

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Inherited from Serializable

Inherited from Serializable

Inherited from Logging

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