As at
http://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html
Prior to shuffling, `X` stacks a number of these primary "informative"
features, "redundant" linear combinations of these, "repeated"
duplicates
of sampled features, and arbitrary noise for and remaining features.
If you set shuffle=False, then you can extract the first n_informative
columns as the primary informative features, etc.
HTH
On 28 May 2015 at 19:18, Daniel Homola <daniel.homol...@imperial.ac.uk>
wrote:
> Hi everyone,
>
> I'm benchmarking various feature selection methods, and for that I use
> the make_classification helper function which really great. However, is
> there a way to retrieve a list of the informative and redundant features
> after generating the fake data? It would really interesting to see, if
> the algorithm I'm working on is able to tell the difference between
> informative and redundant ones.
>
> Cheers,
> Daniel
>
>
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