Hi, Jason,
like Andreas said, you really have to be careful with categorical features. I
think the one-hot-encoder is more for nominal features though, I would handle
ordinal ones differently:
E.g., if you have "sizes" like "M", "L", "S", "XL", I would encode them as
["M", "L", "S", "XL"] -> [
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
