[pymvpa] Combinatorial MVPA

[Richard Dinga]

Bill Broderick wrote:

> However, to determine which timecourse is contributing the most to the
> classifiers performance,

> see which timecourses or which combination
> of time courses caused the greatest drop in performance when removed.

I wrote:
> You might take a look at Relief algorithm (also implemented in PyMVPA),
> that is less hacky approach to your feature weighting problem.


Yaroslav Halchenko wrote:

> there is yet another black hole of methods to assess contribution of
> each feature to performance of the classifier.  The irelief, which was
> mentioned is one of them...

> So what is your classification performance if you just do
> classsification on all features?  which one could you obtain if you do
> feature selection, e.g. with SplitRFE (which would eliminate features to
> attain best performance within each cv folds in nested cv)


I think there are (at least) 2 separate problems.

1. How to evaluate predictive power for every feature in order to interpret
data
2. How to evaluate importance of features for a classifier in order to
understand a model and possibly select set of features to get best
performance.

Feature selection methods like Lasso or RFE (as far as I know) would omit
most of redundant/higly correlated features, therefore making a 1.
impossible. It still might me a good idea for other reasons.
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