*Hi *Maheshakya,
It's hard to know what you mean by the rankings not making sense. What
parameters do you pass to the RFE constructor? In particular, what
underlying estimator are you using, and what step?
I'll take a guess at the problem:
In each iteration, RFE discards the `step` worst features until
`n_features_to_select` features remain. Worst features is determined from
the magnitute of that feature's coefficients in the model learnt from the
remaining features at each step. If your estimator produces a sparse model
(e.g. L1 or strong L2 regularisation), many of your features are likely to
have equal 0 coefficients, and yet only an arbitrary selection of `step`
0-coefficient features will be removed in each iteration. This is unlikely
to be a problem if you haven't one-hot-encoded your categorical data.
Does that help?
- Joel
On Tue, Jul 9, 2013 at 2:15 PM, Maheshakya Wijewardena <
[email protected]> wrote:
> 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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