Thanks for your response Fred. 
  
On Jan 24, 2014, at 12:09 AM, Fred Mailhot <[email protected]> wrote:

> Is your aim to use this information for feature selection, or do you actually 
> want to see which features are being maximally weighted? There's a SO 
> question that addresses the latter use:
> 
> http://stackoverflow.com/questions/11116697/how-to-get-most-informative-features-for-scikit-learn-classifiers
> 
> 
> 
> On 23 January 2014 12:30, Kyle Kastner <[email protected]> wrote:
> Some classifiers (several of the tree based ones - RandomForest, 
> GradientBoostingRegressorTree) have a clf.feature_importances_ which can be 
> plotted to show the relative strength of each feature. sklearn.ensemble also 
> has a module called partial_dependence, which has a function 
> plot_partial_dependence, which plots feature importances against each other 
> as a "heat map". Maybe these will help you?
> 
> 
> On Thu, Jan 23, 2014 at 2:19 PM, Arman Eshaghi <[email protected]> 
> wrote:
> Dear all,
> 
> I was wondering whether there is a sensitivity analyzer inside scikit-learn? 
> I saw recursive feature elimination, but I would like to see which features 
> are the most important for classification in my data.
> 
> All the best,
> Arman
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