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scikit-learn 1.3.2
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# Computation times

00:20.794 total execution time for auto_examples_gaussian_process files:

Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR) (`plot_gpr_co2.py`) | 00:08.738 | 0.0 MB
Gaussian process classification (GPC) on iris dataset (`plot_gpc_iris.py`) | 00:02.751 | 0.0 MB
Comparison of kernel ridge and Gaussian process regression (`plot_compare_gpr_krr.py`) | 00:02.534 | 0.0 MB
Ability of Gaussian process regression (GPR) to estimate data noise-level (`plot_gpr_noisy.py`) | 00:01.842 | 0.0 MB
Illustration of Gaussian process classification (GPC) on the XOR dataset (`plot_gpc_xor.py`) | 00:01.557 | 0.0 MB
Probabilistic predictions with Gaussian process classification (GPC) (`plot_gpc.py`) | 00:01.418 | 0.0 MB
Illustration of prior and posterior Gaussian process for different kernels (`plot_gpr_prior_posterior.py`) | 00:01.280 | 0.0 MB
Gaussian Processes regression: basic introductory example (`plot_gpr_noisy_targets.py`) | 00:00.349 | 0.0 MB
Gaussian processes on discrete data structures (`plot_gpr_on_structured_data.py`) | 00:00.170 | 0.0 MB
Iso-probability lines for Gaussian Processes classification (GPC) (`plot_gpc_isoprobability.py`) | 00:00.156 | 0.0 MB