Yeah - another vote here to do what's called One-Hot encoding, just convert
the single categorical feature into N columns, where N is the number of
distinct values of that feature, with a single one and all the other
features/columns set to zero.
On Tue, Sep 16, 2014 at 2:16 PM, Sean Owen wrote:
I think it's on the table but not yet merged?
https://issues.apache.org/jira/browse/SPARK-1216
On Tue, Sep 16, 2014 at 10:04 PM, st553 wrote:
> Does MLlib provide utility functions to do this kind of encoding?
>
>
>
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Does MLlib provide utility functions to do this kind of encoding?
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I see. So, basically, kind of like dummy variables like with regressions.
Thanks, Sean.
On Jul 11, 2014, at 10:11 AM, Sean Owen wrote:
> Since you can't define your own distance function, you will need to
> convert these to numeric dimensions. 1-of-n encoding can work OK,
> depending on your
Since you can't define your own distance function, you will need to
convert these to numeric dimensions. 1-of-n encoding can work OK,
depending on your use case. So a dimension that takes on 3 categorical
values, becomes 3 dimensions, of which all are 0 except one that has
value 1.
On Fri, Jul 11,
