This is still true. The limitation is not with the cross-fold learner, but with the adaptive learners fitness function.
You should be able to get useful work done with the non-adaptive version, OnlineLogisticRegression, which doesn't require AUC to work. On Thu, Dec 29, 2011 at 6:32 PM, Lance Norskog <[email protected]> wrote: > AdaptiveLogisticRegression includes the following note about > beingworthwhile for binary values only. Is this still true, or are > there alternative crossfold learners for multinomial data? > > org.apache.mahout.classifier.sgd.AdaptiveLogisticRegression ---------- > * The fitness used here is AUC. Another alternative would be to > try log-likelihood, but it is much * easier to get bogus values of log-likelihood than with AUC and the > results seem to accord pretty * well. It would be nice to allow the fitness function to be pluggable. > This use of AUC means that * AdaptiveLogisticRegression is mostly suited for binary target variables. > This will be fixed * before long by extending OnlineAuc to handle non-binary cases or by using > a different fitness * value in non-binary cases. --------------- > > -- > Lance Norskog > [email protected] >
