Hi everyone, I have this (rather vague) intuition that studying the "reasons" which led a trained classifier to behave like it did on particular instances of a problem might be a good way to increase its understanding. If you have for instance a very imbalanced problem, it might be useful to identify the few cases where a (trained) classifier answered right (in terms of classification or probabilistic output) on the least likely class, in order to determine which particular features have played a positive role, and which haven't. The way I see it, this would be a bit like "reverse engineering the features".
So my question: is there a mechanism or maybe an already existing framework or theory for doing this? And would something approaching it be possible currently with Sklearn? Thanks, Christian ------------------------------------------------------------------------------ Got visibility? Most devs has no idea what their production app looks like. Find out how fast your code is with AppDynamics Lite. http://ad.doubleclick.net/clk;262219671;13503038;y? http://info.appdynamics.com/FreeJavaPerformanceDownload.html _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
