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

   @native() @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.  def generativeValidation[T](source: Iterable[(Vector[Any], T)], learner: Learner, nTrain: Int, nTest: Int, nRound: Int): Iterator[(PredictionResult[T], Seq[T])]

   Generate predicted-vs-actual data given a source of ground truth data and a learner

   Generate predicted-vs-actual data given a source of ground truth data and a learner

   Each predicted-vs-actual set (i.e. item in the returned iterable) comes from:

   • Drawing nTrain points from the source iterator
   • Training the learner on those nTrain points
   • Drawing nTest more points to form a test set
   • Applying the model to the test set inputs, and zipping with the test set ground truth responses which is repeated nRound times

   T

   type of the model

   source

   of the training and test data

   learner

   to validate

   nTrain

   size of each training set

   nTest

   size of each test set

   nRound

   number of train/test sets to draw and evaluate

   returns

   predicted-vs-actual data that can be fed into a metric or visualization

10.  def generativeValidation[T](source: Iterator[(Vector[Any], T)], learner: Learner, nTrain: Int, nTest: Int, nRound: Int): Iterator[(PredictionResult[T], Seq[T])]

    Generate predicted-vs-actual data given a source of ground truth data and a learner

    Generate predicted-vs-actual data given a source of ground truth data and a learner

    Each predicted-vs-actual set (i.e. item in the returned iterable) comes from:

    • Drawing nTrain points from the source iterator
    • Training the learner on those nTrain points
    • Drawing nTest more points to form a test set
    • Applying the model to the test set inputs, and zipping with the test set ground truth responses which is repeated nRound times

    T

    type of the model

    source

    of the training and test data

    learner

    to validate

    nTrain

    size of each training set

    nTest

    size of each test set

    nRound

    number of train/test sets to draw and evaluate

    returns

    predicted-vs-actual data that can be fed into a metric or visualization

11.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

12.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

13.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

14.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

15.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

16.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

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

    Definition Classes

    AnyRef

18.  def toString(): String

    Definition Classes

    AnyRef → Any

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

    @native() @throws( ... )