MLRunner

abstract def predict(conf: Configuration): Unit

Using a model to predict with unobserved samples

abstract def train(conf: Configuration): Unit

Training job to obtain a model

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

final def predict(conf: Configuration, model: MLModel, taskClass: Class[_ <: BaseTask[_, _, _]]): Unit

Default predict method with standard predict process.

def submit(conf: Configuration): Unit

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

def toString(): String

final def train(conf: Configuration, model: MLModel, taskClass: Class[_ <: BaseTask[_, _, _]]): Unit

Default train method with standard training process.

final def wait(): Unit

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

final def wait(arg0: Long): Unit