[pymvpa] classifier prediction question

[Jason Ozubko]

Terrific. I just took a quick look but I think this is just what I need.

Thanks!

Cheers,
Jason




On Thu, Jan 16, 2014 at 10:21 PM, Yaroslav Halchenko
<debian at onerussian.com>wrote:

> look into a classifier ca.estimates.  Some classifiers (e.g. SMLR, GNB)
> would base their decision on e.g. a posterior probability which would
> then be stored in the clf.ca.estimates for a classifier clf upon making
> a prediction. E.g.
>
> In [18]: clf = mv.SMLR(enable_ca=['predictions'])
>
> In [19]: clf.train(mvtd.datasets['uni3small'])
>
> In [20]: clf.predict(mvtd.datasets['uni3small'])
> Out[20]:
> array(['L0', 'L0', 'L0', 'L0', 'L0', 'L0', 'L0', 'L0', 'L0', 'L0', 'L0',
>        'L0', 'L1', 'L1', 'L1', 'L1', 'L1', 'L1', 'L1', 'L1', 'L1', 'L1',
>        'L1', 'L1', 'L2', 'L2', 'L2', 'L2', 'L2', 'L2', 'L2', 'L2', 'L2',
>        'L2', 'L2', 'L2'],
>       dtype='|S2')
>
> In [21]: print clf.ca.estimates
> [[  9.98840082e-01   7.72142962e-04   3.87774658e-04]
>  [  9.97071204e-01   2.78187822e-03   1.46917290e-04]
>  [  9.89887463e-01   4.86107005e-03   5.25146706e-03]
>  [  9.96544159e-01   1.23337390e-03   2.22246665e-03]
>  [  9.76508361e-01   2.31793063e-03   2.11737084e-02]
>  [  8.52440274e-01   4.06182039e-02   1.06941522e-01]
>  [  9.99943827e-01   1.12451619e-05   4.49279579e-05]
>
>
>
> On Thu, 16 Jan 2014, Jason Ozubko wrote:
>
> >    Perhaps a very newbie question but when you call clf.predict is it
> >    possible to have the function return more than just a single
> prediction?
> >    As in, if I have 4 target labels, is it possible to get, for each test
> >    sample, the probability (or some other metric) with which the
> classifier
> >    thinks that each of those 4 target labels apply?
> >    So for example, if you had target types of "animal", "vegetable",
> >    "mineral", and "person" and you trained up a classifier, then with
> >    clf.predict I could submit a handful of test samples and get results
> like�
> >    ["vegetable"
> >    "vegetable"
> >    "animal"
> >    "person"
> >    "mineral"
> >    "mineral"]
> >    But is there any way to instead get a read out that says something
> like,
> >    for the first sample the classifier would have picked vegetable first,
> >    then animal, then person, and lastly mineral. �For the second sample
> >    however the classifier would have picked vegetable then person, then
> >    animal, then mineral? �So I could see not only what option the model
> >    predicts but also how close was each test sample to the other options
> as
> >    well?
> >    Thanks in advance
> >    Cheers,
> >    Jason
>
> > _______________________________________________
> > Pkg-ExpPsy-PyMVPA mailing list
> > Pkg-ExpPsy-PyMVPA at lists.alioth.debian.org
> >
> http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
>
>
> --
> Yaroslav O. Halchenko, Ph.D.
> http://neuro.debian.net http://www.pymvpa.org http://www.fail2ban.org
> Senior Research Associate,     Psychological and Brain Sciences Dept.
> Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
> Phone: +1 (603) 646-9834                       Fax: +1 (603) 646-1419
> WWW:   http://www.linkedin.com/in/yarik
>
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