Hi Stuart, I don't think so because there is no standard way to compute CI's. That goes for all performance measures (accuracy, precision, recall, etc.). Some people use simple binomial approximation intervals, some people prefer bootstrapping etc. And it also depends on the data you have. In large datasets, binomial approximation intervals may be sufficient and bootstrapping too expensive etc.
Thanks for sharing that paper btw, will have a look. Best, Sebastian > On Feb 6, 2019, at 11:28 AM, Stuart Reynolds <stu...@stuartreynolds.net> > wrote: > > https://papers.nips.cc/paper/2645-confidence-intervals-for-the-area-under-the-roc-curve.pdf > Does scikit (or other Python libraries) provide functions to measure the > confidence interval of AUROC scores? Same question also for mean average > precision. > > It seems like this should be a standard results reporting practice if a > method is available. > > - Stuart > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
