Thanks for the interest guys I'll try to address some of your comments.
I haven't pushed the code anywhere yet. Putting aside potential API issues,
there are currently no tests, there may be some numerical issues that still
need to be ironed out, some data types were specialized for cython for the
Jacques, is your LambdaMART implementation available somewhere?
Mathieu
On Thu, Nov 7, 2013 at 12:09 AM, Mathieu Blondel wrote:
> On a related note, I implemented NDCG with a slightly different interface
> than Olivier's implementation:
> https://gist.github.com/mblondel/7337391
>
> My implemen
On a related note, I implemented NDCG with a slightly different interface
than Olivier's implementation:
https://gist.github.com/mblondel/7337391
My implementation takes y_true and y_pred as arguments and so is more
consistent with other metrics in scikit-learn. However y_pred might not be
availab
This is very interesting. I have been playing recently with learning
to rank. Right now I just used point-wise regressors and just
implemented NDCG as a ranking metric to compare the models. I tried to
experiment with parallelizing extra trees here:
http://nbviewer.ipython.org/urls/raw.github.c
Hi Jacques,
very exciting -- this was on my wish list for quite a while.
maybe we should start creating a PR upfront so that we can discuss things
there -- better than using the mailing list (quite a lot of traffic
already).
The most important part of adding lambdaMart to sklearn is fleshing out a
Hello scikit-learn,
I recently wrote up an implementation of the LambdaMART algorithm on top of
the existing gradient boosting code (thanks for the great base of code to
work with btw). It currently only supports NDCG but it would be easy to
generalize. That's kind of besides the point however. Be
