case object ExponentialLoss extends GBMClassificationLoss with GBMScalarLoss with HasScalarHessian with Product with Serializable
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- GBMLoss.scala
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- ExponentialLoss → GBMScalarLoss → GBMLoss
- def encodeLabel(label: Double): Array[Double]
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- ExponentialLoss → GBMLoss
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- def gradient(label: Double, prediction: Double): Double
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- ExponentialLoss → GBMScalarLoss
- def gradient(label: Array[Double], prediction: Array[Double]): Array[Double]
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- GBMScalarLoss → GBMLoss
- def hessian(label: Double, prediction: Double): Double
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- ExponentialLoss → HasScalarHessian
- def hessian(label: Array[Double], prediction: Array[Double]): Array[Double]
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- HasScalarHessian → HasHessian
- final def isInstanceOf[T0]: Boolean
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- def loss(label: Double, prediction: Double): Double
- Definition Classes
- ExponentialLoss → GBMScalarLoss
- def loss(label: Array[Double], prediction: Array[Double]): Double
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- GBMScalarLoss → GBMLoss
- final def ne(arg0: AnyRef): Boolean
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- def negativeGradient(label: Double, prediction: Double): Double
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- GBMScalarLoss
- def negativeGradient(label: Array[Double], prediction: Array[Double]): Array[Double]
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- GBMLoss
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- def productElementName(n: Int): String
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- Product
- def productElementNames: Iterator[String]
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- Product
- def raw2probabilityInPlace(rawPrediction: Vector): Vector
- Definition Classes
- ExponentialLoss → GBMClassificationLoss
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