hi alex,
what you observe seem to be a huge variance of the estimator.
>
> I would smooth the fMRI data (I assume it is) to stabilize the anova
> feature
> selection.
>
unfortunately these are measures associated with structural features of the
brain that are matched across subjects. i could try normalizing by brain
volume or something.
subject (28) x features (300)
cheers,
satra
>
> let me know if this helps
>
> Alex
>
> On Wed, Mar 14, 2012 at 9:27 PM, Satrajit Ghosh <[email protected]> wrote:
> > i have a situation i cannot quite comprehend. any suggestions would be
> much
> > appreciated.
> >
> > ---
> > clf = sk.svm.SVC(kernel='linear', shrinking=True)
> > anova_filter = SelectKBest(f_regression, k=50)
> > clf = Pipeline([('anova', anova_filter), ('svc', clf)])
> > maxkfold = np.min(np.bincount(y1)[1:])
> > result = []
> > for train, test in cv.StratifiedKFold(y1, maxkfold):
> > result.append((y1[test],
> > clf.fit(X1[train], y1[train]).predict(X1[test])))
> > ---
> >
> > with k=50 in the above anova filter i get the following confusion matrix
> >
> > [[ 0 7]
> > [ 0 10]]
> >
> >
> > with k=51 and greater, i get ( i love this - but i don't have much
> > confidence in it):
> >
> > [[7 0]
> > [1 9]]
> >
> > if i throw a Normalizer in the pipeline:
> >
> > clf = Pipeline([('xfm', Normalizer()), ('anova', anova_filter), ('svc',
> > clf)])
> >
> > then i get the pattern with k=50 above independent of what i set k to.
> >
> > if i do shufflesplit it results in the same issues.
> >
> > cheers,
> >
> > satra
> >
> >
> >
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