Dear sklearn folks, Some time ago (almost two years now :D) I started working on this pr https://github.com/scikit-learn/scikit-learn/pull/10117 ; The starting point was an issue created by Andreas Mueller https://github.com/scikit-learn/scikit-learn/issues/8614. Andreas and Joel Nothman helped with reviewing all iterations up to a few months ago but for the most part the pr has been stalled. I'd like to ask the community if there is still interest for this to move forward? I think in today's state of ML decision thresholding is important; Day to day data science work frequently requires thinking about thresholds and many times people settle for intuitive solutions or thresholds that are not tested for generalisation. Furthermore using more advanced ways for choosing thresholds is not trivial. The increased interest in fairness in ML also points to the idea that thresholding is important. I am not sure if this is the best place to ask but I wasn't sure how else I could find out if people are interested in reviewing the work. I'd like to put some effort into finalising the feature and potentially adapt it to the current state of the art. Would something like that http://papers.nips.cc/paper/6374-equality-of-opportunity-in-supervised-learn... be of interest? Looking forward to your responses Gr, Prokopis