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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