Thanks for the prompt reply, Peter. I may be in a situation that will call
for SGDClassifier, so I have two follow-up questions:
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.
On 15 June 2012 16:53, Peter Prettenhofer <[email protected]>wrote:
> Hi Fred,
>
> the major difference is the optimization algorithm:
> Liblinear/Coordinate Descent vs. Stochastic Gradient Descent.
>
> If your problem is high dimensional (10K or more) and you have a large
> number of examples (100K or more) you should choose the latter -
> otherwise, LogisticRegression should be fine.
>
> Both are not proper multinomial logistic regression models;
> LogisticRegression does not care and simply computes the probability
> estimates of each OVR classifier and normalized to make sure they sum
> to one. You could do the same for SGDClassifier(loss='log') but you
> have to implement it on your own. You should be aware of the fact that
> SGDClassifier(n_jobs > 1) uses multiple processes, thus, if your
> dataset (``X``) is too large (more than 50% of your RAM) you'll run
> into troubles.
>
> best,
> Peter
>
>
> 2012/6/15 Fred Mailhot <[email protected]>:
> > Dear all,
> >
> > What are the advantages of choosing one of the Subject line classifiers
> over
> > the other? At a quick glance, I see the following:
> >
> > - LogisticRegression implements predict_proba for the multiclass case,
> while
> > SGDClassifier doesn't
> > - SGDClassifier(loss="log") lets you specify multiple CPUs for the OVA
> > training, while LogisticRegression doesn't
> >
> > Are there other obvious differences that might influence this decision?
> >
> > Regards,
> > Fred.
> >
> >
> >
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>
>
> --
> Peter Prettenhofer
>
>
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