Weka:W-GaussianProcesses

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Implements Gaussian processes for regression without hyperparameter-tuning.

Contents

Description

Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel.

Input

  • training set: expects: ExampleSet


Output

  • model:
  • exampleSet:


Parameters

  • D:
    If set, classifier is run in debug mode and may output additional info to the console
    Range: boolean; default: false
  • L:
    Level of Gaussian Noise wrt transformed target. (default 1)
    Range: real; -?-+?
  • N:
    Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)
    Range: real; -?-+?
  • K:
    The Kernel to use. (default: weka.classifiers.functions.supportVector.PolyKernel)
    Range: string; default: 'weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0'


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