OptimizableObjective

trait OptimizableObjective[Prediction, Output] extends AnyRef

Abstract trait for any (sub)differentiable objective function used to train predictors. More accurately, this defines a family of objective functions indexed by each possible label. Note that these are concave objective functions, not convex loss functions.

Prediction: The type of the prediction: is it a tensor or a scalar?
Output: The type of the output/label: is it integer or real-valued or tensor-valued?

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AnyRef, Any

Known Subclasses

AbsoluteUnivariate, EpsilonInsensitiveAbsMultivariate, EpsilonInsensitiveAbsUnivariate, EpsilonInsensitiveSqMultivariate, EpsilonInsensitiveSqUnivariate, HingeBinary, HingeMulticlass, HingeScaledBinary, HingeSqMulticlass, LogBinary, LogMulticlass, MultivariateOptimizableObjective, SmoothHingeBinary, SparseLogMulticlass, SquaredMultivariate, SquaredUnivariate, UnivariateOptimizableObjective

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abstract def valueAndGradient(prediction: Prediction, label: Output): (Double, Prediction)

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final def !=(arg0: AnyRef ): Boolean

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OptimizableObjective

trait OptimizableObjective[Prediction, Output] extends AnyRef

Abstract Value Members

abstract def valueAndGradient(prediction: Prediction, label: Output): (Double, Prediction)

Concrete Value Members

final def !=(arg0: AnyRef ): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef ): Boolean

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 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

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