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 :)
Slightly off-topic, but this is exactly what's necessary to use existing regression algorithms for Bayesian optimization, even internally for hyper-parameter optimization. So not crazy to think about adding such an API. Also, I think it at least a few cases it's more efficient to ask for both the mean and variance of a conditional prediction at the same time, rather than asking first for the mean, and then second for the variance. They can often share a lot of computation (GP is one example). What would be the most scikits-friendly way of dealing with that? - James ------------------------------------------------------------------------------ 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
