LogisticRegression

Value Members

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

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

   AnyRef → Any

3. final def ==(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

4. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

5. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

6. final def eq(arg0: AnyRef): Boolean

   

   Definition Classes

   AnyRef

7. def equals(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

8. def finalize(): Unit

   

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( classOf[java.lang.Throwable] )

9. final def getClass(): Class[_]

   

   Definition Classes

   AnyRef → Any

10. def hashCode(): Int

    

    Definition Classes

    AnyRef → Any

11. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

12. final def ne(arg0: AnyRef): Boolean

    

    Definition Classes

    AnyRef

13. final def notify(): Unit

    

    Definition Classes

    AnyRef

14. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

15. final def synchronized[T0](arg0: ⇒ T0): T0

    

    Definition Classes

    AnyRef

16. def toString(): String

    

    Definition Classes

    AnyRef → Any

17. def trainMIS(input: RDD[(Long, LabeledPoint)], numIterations: Int, stepSize: Double, regParam: Double, epsilon: Double = 1e-3, useAdaGrad: Boolean = false, storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK): LogisticRegressionModel

    

    Modified Iterative Scaling The referenced paper: A comparison of numerical optimizers for logistic regression http://research.microsoft.com/en-us/um/people/minka/papers/logreg/minka-logreg.pdf

    Modified Iterative Scaling The referenced paper: A comparison of numerical optimizers for logistic regression http://research.microsoft.com/en-us/um/people/minka/papers/logreg/minka-logreg.pdf

    input

    training data, feature value must >= 0, label is either 0 or 1 (binary classification)

    numIterations

    maximum number of iterations

    stepSize

    step size, recommend to be in value range 0.1 - 1.0

    regParam

    L1 Regularization

    epsilon

    smoothing parameter, 1e-4 - 1e-6

    useAdaGrad

    adaptive step size, recommend to be true

    storageLevel

    recommendation configuration: MEMORY_AND_DISK for small/middle-scale training data, and DISK_ONLY for super-large-scale data

18. def trainSGD(input: RDD[(Long, LabeledPoint)], numIterations: Int, stepSize: Double, regParam: Double, useAdaGrad: Boolean = false, storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK): LogisticRegressionModel

    

    :: Experimental :: SGD training

    :: Experimental :: SGD training

    input

    training data, with {0,1} label (binary classification)

    numIterations

    maximum number of iterations

    stepSize

    learning step size, recommend to be 0.1 - 1.0

    regParam

    L1 Regularization

    useAdaGrad

    adaptive step size, recommend to be True

    storageLevel

    recommendation configuration: MEMORY_AND_DISK for small/middle-scale training data, and DISK_ONLY for super-large-scale data

    Annotations

    @Experimental()

19. final def wait(): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

20. final def wait(arg0: Long, arg1: Int): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

21. final def wait(arg0: Long): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )