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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