RandomForest

abstract def extractDependent(table: RandomForestLibModule.Table): M[Array[A]]

abstract def findError(actual: Array[A], predicted: Array[A]): Double

abstract def forest(trees: Seq[DecisionTree[A]]): F

abstract def makeColumns(defined: BitSet, values: Array[A]): Map[ColumnRef, Column]

abstract def makeTree(dependent: Array[A], independent: Array[Array[Double]]): DecisionTree[A]

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def alignCustom(t1: RandomForestLibModule.Table, t2: RandomForestLibModule.Table): M[(RandomForestLibModule.Table, RandomForestLib.Morph1Apply)]

lazy val alignment: Custom

This specifies how to align the 2 arguments as they are inputted.

final def asInstanceOf[T0]: T0

val chunkSize: Int

def clone(): AnyRef

val dependent: String

val dependentSpec: DerefObjectStatic[Source.type]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

lazy val fqn: String

final def getClass(): Class[_]

def hashCode(): Int

val idPolicy: Merge.type

val independent: String

val independentSpec: DerefObjectStatic[Source.type]

final def isInstanceOf[T0]: Boolean

def makeForest(table: RandomForestLibModule.Table, tpe: JType, prev: F = Monoid[F].zero): M[F]

def makeForests(table0: RandomForestLibModule.Table): M[Seq[(JType, F)]]

val maxForestSize: Int

def morph1Apply(forests: Seq[(JType, F)]): RandomForestLib.Morph1Apply

val multivariate: Boolean

val name: String

val namespace: Vector[String]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val numChunks: Int

val opcode: Int

val rowLevel: Boolean

val sampleSize: Int

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

def toString(): String

val tpe: BinaryOperationType

val varianceThreshold: Double

final def wait(): Unit

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

final def wait(arg0: Long): Unit