ClassificationModel

abstract def fit(dataSet: DataSet, initWeights: Option[INDArray]): Unit

fit weights to the data provided by the implementation of the model

abstract def predict(inputVector: INDArray): Float

predict the probability of positive class for one vector of data

abstract def predictClass(inputVector: INDArray): Float

predict class (1 or 0) for one vector of data

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

var losses: Seq[Float]

contains changes of the loss function value during optimisation at every step

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

var weights: INDArray

contains weights after the fitting the model