Hey Fred.
About 1): Yes, you could to that. I am not sure if you would get
meaningful results, though.
I would use a softmax activation, i.e. doing exp of the decision
function before normalization.
Otherwise it might be negative and I'm not sure how you would normalize
that.
2) There is a partial_fi
<http://scikit-learn.org/dev/modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier.partial_fit>t
implemented for this purpose.
Cheers,
Andy
On 06/17/2012 01:15 PM, Fred Mailhot wrote:
Dear all,
Just *bump*ing my last two questions. Apologies if this is considered
poor etiquette...
Thanks!
---------- Forwarded message ----------
From: *Fred Mailhot* <[email protected]
<mailto:[email protected]>>
Date: 15 June 2012 17:22
[...]
1) I'd like to compute the class probs; are the probs for the
individual OvR classifiers (easily) accessible? My intuition is that I
can compute these from the returned vals from decision_function(),
then do the normalization afterward...
2) How "online" is the SGD implementation? Specifically, would it be
possible do to something like continuous training from a "neverending"
stream of data (e.g. coming in over a network socket)?
Thanks again,
Fred.
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