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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