On 11/17/2012 03:41 PM, Ronnie Ghose wrote: > Hmmm interesting so I could run > ex: > Naive Bayes, > Bayesian Nets > Boosting + Bagging > Generalized Unsupervised Learning > > on subsets O_O? The idea with trees and subsets is that you work with an ensemble any way (a random forest). So you can train each classifier in the ensemble on a different subset. For other classifiers, for example Naive Bayes, it is easily possible to construct the classifier "in the usual way" without holding all data in the memory at the same time.
I am not sure what your question is. Also, are you asking about sklearn or how to train models in general? ------------------------------------------------------------------------------ Monitor your physical, virtual and cloud infrastructure from a single web console. Get in-depth insight into apps, servers, databases, vmware, SAP, cloud infrastructure, etc. Download 30-day Free Trial. Pricing starts from $795 for 25 servers or applications! http://p.sf.net/sfu/zoho_dev2dev_nov _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
