Say that I am trying to determine which customers buy particular candy bars. So I want to classify training data consisting of candy bar attributes (an N dimensional vector of variables) into customer attributes (an M dimensional vector of customer attributes).
Is there a preferred method when N and M are large? That is say 100 or more? I have done binary classification using AdaptiveLogisticRegression and OnlineLogisticRegression and small numbers of input features with relative success. As I'm trying to implement this for large N and M, I feel like i'm veering into the woods. Is there a code example anyone can point me to that uses mahout libraries to do multi-class classification when the number of classes is large?
