Hi,
I'm trying to use Recursive Feature Elimination to a data set ( it's a very
large matrix after performing one hot encoding).
suppose One Hot encoded matrix is " X " (We have targets in y)
rfe = RFE(some parameters)
rfe.fit(X,y)
After this I can get indices of selected features by rankings_ (or mask by
support_)
I want to know what are the values we get buy above when One hot encoded
matrix is considered (as it is a binary sparse matrix). The indices I get
from rankings_ (where ranking is 1 or mask is true) don't make any sense
when One hot encoded data is matrix is considered.
Can someone explain how to solve this?
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