Gaussian Process-based Regression (GPR) System

A Gaussian Process-based Regression (GPR) System is a nonparametric regression system which implements a GPR algorithm to solve a GPR task.

• AKA: Kriging Regressor.
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• Example(s):
  • sklearn.gaussian_process.GaussianProcessRegressor.
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• Counter-Examples:
  • A Linear Ridge Regression System.
  • A Gaussian Process-based Classification (GPC) System.
  • A NonLinear Least-Squares Regression System.
• See: Gaussian Process, Nonparametric Regression, Kernel Method.

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References

2017a

• http://scikit-learn.org/stable/modules/gaussian_process.html
  • QUOTE: The GaussianProcessRegressor implements Gaussian processes (GP) for regression purposes. For this, the prior of the GP needs to be specified. The prior mean is assumed to be constant and zero (for normalize_y=False) or the training data’s mean (for normalize_y=True). The prior’s covariance is specified by a passing a kernel object. The hyperparameters of the kernel are optimized during fitting of GaussianProcessRegressor by maximizing the log-marginal-likelihood (LML) based on the passed optimizer. As the LML may have multiple local optima, the optimizer can be started repeatedly by specifying n_restarts_optimizer. The first run is always conducted starting from the initial hyperparameter values of the kernel; subsequent runs are conducted from hyperparameter values that have been chosen randomly from the range of allowed values. If the initial hyperparameters should be kept fixed, None can be passed as optimizer.

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