Jacques, is your LambdaMART implementation available somewhere?
Mathieu
On Thu, Nov 7, 2013 at 12:09 AM, Mathieu Blondel <[email protected]>wrote:
> 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
> available for listwise methods so Olivier's implementation is useful too.
>
> For learning to rank with only two relevance levels (0 and 1) we already
> have two metrics in scikit-learn: ROC-AUC and average precision.
>
> I just pushed an alternative implementation of average precision in the
> unit tests so as to check for the correctness of scikit-learn's
> implementation (based on computing the area under the precision-recall
> curve):
>
> https://github.com/scikit-learn/scikit-learn/commit/d0cdcde9c500f5c9d73f61b97e0e69410fc694ef
>
> Cheers,
> Mathieu
>
>
> On Wed, Nov 6, 2013 at 8:30 PM, Olivier Grisel
> <[email protected]>wrote:
>
>> 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.com/ogrisel/notebooks/master/Learning%20to%20Rank.ipynb
>>
>> I think a GradientBoostingRegressor model can reach better accuracy
>> but is not parallizable alone. Off-course if you use list-wise
>> approach directly optimizing the target cost (e.g. NDCG like
>> LambdaMART does) you should be able to reach the state of the art.
>>
>> The data was parsed once and save in compressed format here:
>>
>>
>> http://nbviewer.ipython.org/url/raw.github.com/ogrisel/notebooks/master/Data%20Preprocessing%20for%20the%20%20Learning%20to%20Rank%20example.ipynb
>>
>> Here are the slides I am gonna present this afternoon at Budapest BI
>> Forum:
>>
>> https://speakerdeck.com/ogrisel/growing-randomized-trees-in-the-cloud-1
>>
>> About the API, properly supporting Learning to Rank will have impact
>> on the scorer API and the cross validation / grid search. I am not yet
>> sure how to best address all of this.
>>
>> --
>> Olivier
>> http://twitter.com/ogrisel - http://github.com/ogrisel
>>
>>
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