[scikit-learn] SVM-RFE

Andreas Mueller t3kcit at gmail.com
Fri Nov 22 12:08:16 EST 2019

I think you can also use RFECV directly without doing any wrapping.

On 11/20/19 12:24 AM, Brown J.B. via scikit-learn wrote:
> 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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