hmm i'm asking is it possible to run all of the typical ~ whatever that
means ~ models in sklearn on a subset of that data and have it work pretty
well most of the time?
On 17 November 2012 11:08, Andreas Mueller <[email protected]> wrote:
> 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?
>
>
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