.. _example_gaussian_process_gp_diabetes_dataset.py: ======================================================================== Gaussian Processes regression: goodness-of-fit on the 'diabetes' dataset ======================================================================== In this example, we fit a Gaussian Process model onto the diabetes dataset. We determine the correlation parameters with maximum likelihood estimation (MLE). We use an anisotropic squared exponential correlation model with a constant regression model. We also use a nugget of 1e-2 to account for the (strong) noise in the targets. We compute a cross-validation estimate of the coefficient of determination (R2) without reperforming MLE, using the set of correlation parameters found on the whole dataset. **Python source code:** :download:`gp_diabetes_dataset.py ` .. literalinclude:: gp_diabetes_dataset.py :lines: 21-