Hi Jason.
We don't have any support for groups or types of features currently, sorry.
And you do need to convert all categorical features to one-hot encoded
features for use with sklearn.
The underlying issue is that we use numpy arrays as our main data
structure, and they are not very easy to annotate with feature types etc.
Best,
Andreas
On 03/02/2015 01:08 PM, Jason Wolosonovich wrote:
Hello All,
When using any of the preprocessing options in sklearn, is it possible
to select a subset of features (columns) in a dataset for
preprocessing? Many datasets contain a mix of feature types
(categorical, numerical, binary) and it doesn’t seem like it would
make sense to scale certain types of features (like binary and
categorical), though I suppose if the information contained in them is
not altered by the scaling, it may not hurt to have it scale the
entire dataset regardless of feature type. Any thoughts on the
subject welcome. Thanks!
-Jason
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