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=== Gaussian processes === {{Main|Gaussian processes}} [[Image:Regressions sine demo.svg|thumbnail|right|An example of Gaussian Process Regression (prediction) compared with other regression models<ref>The documentation for [[scikit-learn]] also has similar [http://scikit-learn.org/stable/auto_examples/gaussian_process/plot_compare_gpr_krr.html examples] {{Webarchive|url=https://web.archive.org/web/20221102184805/https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_compare_gpr_krr.html |date=2 November 2022 }}.</ref>]] A Gaussian process is a [[stochastic process]] in which every finite collection of the random variables in the process has a [[multivariate normal distribution]], and it relies on a pre-defined [[covariance function]], or kernel, that models how pairs of points relate to each other depending on their locations. Given a set of observed points, or input–output examples, the distribution of the (unobserved) output of a new point as function of its input data can be directly computed by looking like the observed points and the covariances between those points and the new, unobserved point. Gaussian processes are popular surrogate models in [[Bayesian optimisation]] used to do [[hyperparameter optimisation]].
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