Newton is never d**2 because every body uses a truncated Newton, which is in effect linear in d.
Gaël Sent from my phone. Please forgive brevity and mis spelling On Nov 8, 2015, 18:51, at 18:51, Sebastian Raschka <se.rasc...@gmail.com> wrote: > >> On Nov 8, 2015, at 11:32 AM, Raphael C <drr...@gmail.com> wrote: >> >> In terms of computational efficiency, one-hot encoding combined with >> the support for sparse feature vectors seems to work well, at least >> for me. I assume therefore >> the problem must be in terms of classification accuracy. > >One thing comes to mind regarding the different solvers for the linear >models. E.g., Newton’s method is O(n * d^2), and even gradient descent >is O(n *d) > >For decision trees, I don’t see a substantial difference in terms of >computational complexity if a categorical feature, let’s say it can >take 4 values, is split into 4 binary questions (i.e., using one-hot >encoding). One the other hand, I think the problem is that the decision >algorithm does not no that these 4 binary questions “belong” to one >observation, which could make the decision tree grow much larger in >depth and width; this is bad for computational efficiency and would >more likely produce trees with higher variance. > >I’d be curious how to handle categorical feature columns >implementation-wise though. I think additional parameters in the method >call would be necessary (e.g., .fit(categorical=(1, 4, 19), nominal=(1, >4)) to distinguish ordinal from nominal variables? >Or, alternatively, I think this would be a good use-case for numpy’s >structured arrays? > > > >------------------------------------------------------------------------------ >_______________________________________________ >Scikit-learn-general mailing list >Scikit-learn-general@lists.sourceforge.net >https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
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