[pymvpa] Cross Validation Output

[Yaroslav Halchenko]

On Mon, 15 Apr 2013, Paul Robinson wrote:

> Hi, Yaroslav.

> As follows:

> >>> print fds.summary()
> Dataset: 39x172800 at float32, <sa:
> chunks,targets,time_coords,time_indices>, <fa: voxel_indices>, <a:
> imghdr,imgtype,mapper,voxel_dim,voxel_eldim>
> stats: mean=-0.00263811 std=0.566497 var=0.320918 min=-5.24132 max=5.29731
> No details due to large number of targets or chunks. Increase maxc and
> maxt if desired
> Summary for targets across chunks
>   targets  mean std min max #chunks
>  Control  0.513 0.5  0   1     20
>   Patient   0.487 0.5  0   1     19

I suspect that you have placed each sample into a unique chunk
(where they could actually rightfully belong since this is different
subjects). Then with N-Fold partitioning you are pretty much doing
leave-1-out.  Hence your errors would be either 0 or 1 since you have
only 1 sample you cross-validate into.

print cvte.ca.stats

should give you a better "picture"

but mention that you have only few samples, and lots of features, and
disbalance between conditions, so classifier might just go for the one
with more samples to reduce error etc...

> Sequence statistics for 39 entries from set ['Control', 'Patient']
> Counter-balance table for orders up to 2:
> Targets/Order O1     |  O2     |
>    Control:   19  1  |  18  2  |
>     Patient:     0 18  |   0 17  |
> Correlations: min=-0.95 max=0.9 mean=-0.026 sum(abs)=19

this is irrelevant here since this is independent samples


-- 
Yaroslav O. Halchenko
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