On 02/12/2011 18:19, Alexandre Passos wrote: > On Fri, Dec 2, 2011 at 12:02, James Bergstra<[email protected]> wrote: >> On Tue, Nov 29, 2011 at 5:24 PM, Olivier Grisel >> <[email protected]> wrote: >>> That makes sense. Fortunately we don't have an API to compute the >>> expected variance of a prediction :) > So what does the eval_MSE option do? Indeed, AlexP is right. The eval_MSE kwarg bool does exactly what you want and reuse intermediate variables to make the computation of the prediction's mean and variance optimal (as it is done in DACE for Matlab on which this implementation is based). When eval_MSE is True, the predictor returns a tuple of 2 arrays (one for the mean values and the other for the variance's) that you can split right away. See the following examples: http://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gp_regression.html http://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gp_probabilistic_classification_after_regression.html
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