Class

com.linkedin.photon.ml.hyperparameter.criteria

ExpectedImprovement

Related Doc: package criteria

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class ExpectedImprovement extends PredictionTransformation

Expected improvement selection criterion. This transformation produces the expected improvement of the model predictions (over the current "best" value).

See also

"Practical Bayesian Optimization of Machine Learning Algorithms" (PBO), https://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf

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PredictionTransformation, AnyRef, Any
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Instance Constructors

  1. new ExpectedImprovement(bestEvaluation: Double)

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    bestEvaluation

    The current best evaluation

Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. def apply(predictiveMeans: DenseVector[Double], predictiveVariances: DenseVector[Double]): DenseVector[Double]

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    Applies the expected improvement transformation to the model output.

    Applies the expected improvement transformation to the model output.

    predictiveMeans

    Predictive mean output from the model

    predictiveVariances

    Predictive variance output from the model

    returns

    The expected improvement over the current best evaluation

    Definition Classes
    ExpectedImprovement → PredictionTransformation

  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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  7. final def eq(arg0: AnyRef): Boolean

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  9. def finalize(): Unit

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  10. final def getClass(): Class[_]

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  11. def hashCode(): Int

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  12. final def isInstanceOf[T0]: Boolean

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  13. def isMaxOpt: Boolean

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    ExpectedImprovement → PredictionTransformation

  14. final def ne(arg0: AnyRef): Boolean

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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

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  18. def toString(): String

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  19. final def wait(): Unit

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

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  21. final def wait(arg0: Long): Unit

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Inherited from PredictionTransformation

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