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'