Regressor

abstract val name: String

The name of the regressor

abstract def predict(X: List[List[Double]]): List[Double]

Applies the trained regressor to a dataset

abstract def train(X: List[List[Double]], y: List[Double]): Unit

Performs the training of the regressor

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def diagnostics(): Map[String, List[(Double, Double)]]

Provides meta-information on the regressor

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

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit