Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/4460#issuecomment-74609769
I like the current sketch but also want to think about it more. A few
thoughts:
I'm not quite clear on how the Array of Attributes in FeatureAttributes
corresponds to the columns of the DataFrame. Is it one-to-one, or will
Attributes be nested? (I'm basically thinking about groups of features,
especially individual features grouped into vectors.)
How will propagation of feature names work? Will we try to impose a
standard, such as Transformers maintaining the same (or a modified) feature
name whenever possible?
By the way, do we want to call this "FeatureAttributes," or should we name
it something like "ColumnAttributes" so it more obviously applies to other
types of columns like labels, users, products, etc.?
+1 for moving FeatureType from mllib.tree to attribute. It should be more
general.
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