Regression

abstract def saveTo(writer: Writer): Unit

Saves to writer.

abstract def scoreOf(d: Datum[Double, F]): Double

Returns the score for this datum NB: This is not necessarily a probability

abstract def train(dataset: RegDataset[F], indices: Array[Int]): Unit

Trains a classifier, using only the datums specified in indices indices is useful for bagging

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def saveTo(fileName: String): Unit

Saves the current model to a file

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

def toString(): String

def train(dataset: RegDataset[F], spans: Option[Iterable[(Int, Int)]]): Unit

Trains the classifier on the given dataset spans is useful during cross validation

def train(dataset: RegDataset[F]): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit