On 12/13/18 4:16 AM, Joris Van den Bossche wrote:
Hi all,
I finally had some time to start looking at it the last days. Some
preliminary work can be found here:
https://github.com/jorisvandenbossche/target-encoder-benchmarks.
You continue to be my hero. Probably can not look at it in detail before
the holidays though :-/
Up to now, I only did some preliminary work to set up the benchmarks
(based on Patricio Cerda's code,
https://arxiv.org/pdf/1806.00979.pdf), and with some initial datasets
(medical charges and employee salaries) compared the different
implementations with its default settings.
So there is still a lot to do (add datasets, investigate the actual
differences between the different implementations and results, in a
more structured way compare the options, etc, there are some todo's
listed in the README). However, now I am mostly on holidays for the
rest of December. If somebody wants to further look at it, that is
certainly welcome, otherwise, it will be a priority for me beginning
of January.
For datasets: additional ideas are welcome. For now, the idea is to
add a subset of the Criteo Terabyte Click dataset, and to generate
some data.
>>> Does that mean you'd be opposed to adding the leave-one-out TargetEncoder
>>> I would really like to add it before February
>> A few month to get it right is not that bad, is it?
> The PR is over a year old already, and you hadn't voiced any opposition
> there.
As far as I understand, the open PR is not a leave-one-out TargetEncoder?
I would want it to be :-/
I also did not yet add the CountFeaturizer from that scikit-learn PR,
because it is actually quite different (e.g it doesn't work for
regression tasks, as it counts conditional on y). But for
classification it could be easily added to the benchmarks.
I'm confused now. That's what TargetEncoder and leave-one-out
TargetEncoder do as well, right?
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