Thanks a lot Joel, I missed that bit of the docs!
On 28/05/15 11:00, Joel Nothman wrote:
I should note however that the "informative" features already have
covariance, so their differentiation from the redundant features is
likely hard. One difference is that the covariance is per-class in the
underlying features, whereas the redundant features will vary
identically (disregarding added noise in flip_y) across classes with
respect to the informative features.
On 28 May 2015 at 19:57, Joel Nothman <joel.noth...@gmail.com
<mailto:joel.noth...@gmail.com>> wrote:
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
<mailto: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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