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Make zero_division
parameter consistent in the different metric
#29048
Comments
@StefanieSenger you might be interested in looking at some of the item. |
+1 for this! |
I am working on task-1 (Introduce the zero_division parameter to the accuracy_score function when y_true and y_pred are empty.). |
Thank you @Jaimin020 ! I needed that recently and had to write a wrapper of the accuracy function. |
The intend of this issue is not quite clear to me. @glemaitre, could you clarify: #23183 is about to be merged for the last 1,5 years, but is difficult to review. This issue is supposed to facilitate this process by splitting it into different pull-requests. However, it only lists tasks that would enhance the original PR (or add to it) and depend on it to be merged (except
I can also see that the communication on in the original PR has been very frustrating for its author, because it was unclear, what he needs to deliver in order to push the reviewing process forwards. We should at least define this very clearly right now. This will help everybody involved (and everybody who wants to get involved) know what to expect and be efficient. |
I realised that the solution lies in first splitting up the original PR as proposed by the author. After that, the other tasks from the task list above can be tackled. I will open PRs that split up the original PR during the next days. |
Exactly.
Do not hesitate to tackle a single PR first such that we make sure to get reviews. |
I had missed the fact that the original PR had already mostly been adopted by #25531 and the remaining tasks are actually only those listed in the issue above. The original PR had now been closed by the author to avoid confusion (mine 🤣 ). I will take care of the inividual PR for the |
I am also working on adding the |
I have created a pull request for task-1 (#29213), but the 'codecov/patch' check is failing. Could someone please review it and advise me on how to pass this check? |
This is an issue to report the step to actually take over the work of @marctorsoc in #23183 and split the PR into smaller one to facilitate the review process.
The intend is to make the
zero_division
parameter consistent across different metrics in scikit-learn. In this regards, we have the following TODO list:zero_division
parameter to theaccuracy_score
function wheny_true
andy_pred
are empty.zero_division
parameter foraccuracy_score
#29213zero_division
parameter to theclass_likelihood_ratios
and removeraise_warning
.zero_division
parameter to thecohen_kappa_score
functioncohen_kappa_score
#29210zero_division
parameter to thematthew_corr_coeff
functionnp.nan
inclassification_report
function.All those items have been addressed in #23183 and can be extracted in individual PRs. The changelog presenting the changes should acknowledge @marctorsoc.
In addition, we should investigate #27047 and check if we should add the
zero_division
parameter to theprecision_recall_curve
androc_curve
as well. This might add two additional items to the list above.The text was updated successfully, but these errors were encountered: