On Mon, Dec 5, 2011 at 13:31, James Bergstra <[email protected]> wrote: > I should probably not have scared ppl off speaking of a 250-job > budget. My intuition would be that with 2-8 hyper-parameters, and 1-3 > "significant" hyper-parameters, randomly sampling around 10-30 points > should be pretty reliable.
So perhaps the best implementation of this is to first generate a grid (from the usual arguments to sklearn's GridSearch), randomly sort it, and iterate over these points until the budget is exhausted? Presented like this I can easily see why this is better than (a) going over the grid in order until the budget is exhausted or (b) using a coarser grid to match the budget. This would also be very easy to implement in sklearn. Do I make sense? -- - Alexandre ------------------------------------------------------------------------------ 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
