On Fri, Dec 2, 2011 at 1:00 PM, Vincent Dubourg <[email protected]> wrote: > 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 > > Cheers, >
Cool, good to know! On the name though --- "eval_MSE" is a nonstandard term for "variance" no? MSE usually refers to a loss criterion, for comparing predictions with targets. ------------------------------------------------------------------------------ All the data continuously generated in your IT infrastructure contains a definitive record of customers, application performance, security threats, fraudulent activity, and more. Splunk takes this data and makes sense of it. IT sense. And common sense. http://p.sf.net/sfu/splunk-novd2d _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
