Dear Malik, Your request to do performance checking of the steps of SVM-RFE is a pretty common task.
Since the contributors to scikit-learn have done great to make the interface to RFE easy to use, the only real work required from you would be to build a small wrapper function that: (a) computes the step sizes you want to output prediction performances for, and (b) loops over the step sizes, making each step size the n_features attribute of RFE (and built from the remaining features), making predictions from a SVM retrained (and possibly optimized) on the reduced feature set, and then outputting your metric(s) appropriate to your problem. Tracing the feature weights is then done by accessing the "coef_" attribute of the linear SVM trained. This can be output in loop step (b) as well. where each time 10% for the features are removed. > How one can get the accuracy overall the levels of the elimination stages. > For example, I want to get performance over 1000 features, 900 features, > 800 features,....,2 features, 1 feature. > Just a technicality, but by 10% reduction you would have 1000, 900, 810, 729, 656, ... . Either way, if you allow your wrapper function to take a pre-computed list of feature sizes, you can flexibly change between a systematic way or a context-informed way of specifying feature sizes (and resulting weights) to trace. Hope this helps. J.B. Brown Kyoto University Graduate School of Medicine
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