"GaussianProcess"
(Machine Learning Method)

  • Method for Predict.
  • Infers values by conditioning on the training data a Gaussian process prior over functions.
  • Details & Suboptions
  • The "GaussianProcess" method assumes that the function to be modeled has been generated from a Gaussian process. The Gaussian process is defined by its covariance function (also called kernel). In the training phase, the method will estimate the parameters of this covariance function. The Gaussian process is then conditioned on the training data and used to infer the value of a new example using a Bayesian inference.
  • The following options can be given:
  • AssumeDeterministicAutomaticwhether or not the function should be assumed to be deterministic
    "CovarianceType"Automaticthe covariance type to use
    "EstimationMethod""MaximumPosterior"the method to infer the values
    "OptimizationMethod"Automaticthe optimization method to estimate parameters
  • Possible settings for "CovarianceType" include:
  • "SquaredExponential"exponential kernel
    "HammingDistance"exponential kernel for nominal variables
    "Periodic"periodic kernel
    "RationalQuadratic"rational quadratic kernel
    "Linear"linear kernel
    "Mattern5/2"Matérn kernel with exponent 5/2
    "Mattern3/2"Matérn kernel with exponent 3/2
    "Composite"a composition of the previous kernels
    assocspecify a different kernel for each feature type
  • In Method{"GaussianProcess", "CovarianceType"assoc}, assoc needs to be of the form <|"Numerical" kernel1,"Nominal"kernel2|>.
  • Possible settings for "EstimationMethod" include:
  • "MaximumPosterior"maximize the posterior distribution
    "MaximumLikelihood"maximize the likelihood
    "MeanPosterior"mean of the posterior distribution
  • Possible settings for "OptimizationMethod" include:
  • "SimulatedAnnealing"uses simulated annealing to find the minimum
    "FindMinimum"uses FindMinimum to find the minimum
  • Examples

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    Basic Examples  (2)

    Train a classifier function on labeled examples:

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    Look at the PredictorInformation:

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    Classify a new example:

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    Train a predictor on labeled examples:

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    Compare the data with the predicted values and look at the standard deviation:

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    Options  (4)

    See Also

    Predict  Classify  PredictorInformation  PredictorMeasurements

    Related Methods