> > > you can get perfect misclassification if you have no signal and > classifier is susceptible to disbalance (e.g. stats based classifiers > suchas GNB, LDA etc wouldn't care as much, SVM -- would)... and e.g. > you have perfectly balanced dataset and then do leave-one-sample-out. > So this way you have in training slight disbalance toward one class > which classifier chooses to be the one to assign to any testing > data, in the testing a label of the opposite class - perfect > misclassification > > but there were CS papers about what special layout of data points could > lead to misclassifications. someone would need to search the history of > the list here ;) > > what we see in reality at times (also was reported on the list) is > some biases toward misclassification. Some times they get avoided by > changing partitioning or preprocessing without clearly grasping what > initially lead to it ;) > > fun examples of misclassification are when samples look like XOR or any tiling of that. XOX... OXO...
_______________________________________________ Pkg-ExpPsy-PyMVPA mailing list Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
