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 <[email protected]>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 > > [email protected] > > > 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 > > _______________________________________________ > Pkg-ExpPsy-PyMVPA mailing list > [email protected] > http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
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