hi satra,

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.

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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Cloud computing makes use of virtualization - but cloud computing 
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