[SciPy-User] Classification using neural networks

Gael Varoquaux gael.varoquaux@normalesup....
Thu Jul 26 02:08:33 CDT 2012

On Wed, Jul 25, 2012 at 03:17:23PM -0400, Zachary Pincus wrote:
> Last time I was current with machine learning (ca. 5-7 years ago), the standard advice for the first pass at any particular problem was "throw it at an SVM". 

I think that this is still a good advice. I would say: standardise your
data (for each feature: mean = 0, norm = 1) and throw it into an SVM.

> I don't know if that's still the go-to consensus these days -- can anyone else weight in? (Does some kind of ensemble method routinely beat SVMs these days in the same way that SVMs were routinely beating neural networks in the early 2000s?

If you have heaps of data, you can try random forests or gradient boosted
trees, that work very well.

The scikit-learn has good implementation of all these algorithms, but not
neural-network: they are too old fashion for hip coders to contribute
them ;)

> I guess I should check out abstracts from recent NIPS conferences to find out...)

Nah, NIPS is about mathturbation, not things that work on real data,
these days.


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