2013/7/25 Josh Wasserstein <[email protected]>: > Thank you Olivier. I went through that paper and I agree, it looks like > implementing micro-AUC or macro-AUC should not be that hard. I will try to > implement within the next week. I have have never contributed to a project > in GitHub, so I am not sure to what extent my code would meet the standards > but I am happy to try. > > In the mean time, is there anything similar to an AUC metric that scikit > supports when working with GridSearchCV in a multi-label setting? I am > looking for some compromise between precision and recall that indirectly > optimizes for the AUC score of each label .
You can try the f1 score that is a balanced score (a tradeoff between precision and recall) that is a reasonable score for imbalanced multiclass dataset. It supports both micro and macro averaging. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ See everything from the browser to the database with AppDynamics Get end-to-end visibility with application monitoring from AppDynamics Isolate bottlenecks and diagnose root cause in seconds. Start your free trial of AppDynamics Pro today! http://pubads.g.doubleclick.net/gampad/clk?id=48808831&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
