In my project, I have found that MBR seems to be quite good at identifying my rare event phenomena. From what I have read in the literature, MBR is a nearest neighbor approach, that when dealing with rare events needs boosting/oversampling for training data. People also say that it prefers uncorrelated input variables, and that range scaling is needed. I assume the range scaling is so that variables with a wide range of values do not dominate the decisions by virtue of their size. Can you tell me if MBR takes care of the range scaling, and also removing or diminishing the influence of highly correlated variables? If it does not, then I will need to do extra work to identify these issues and rescale first, or remove via the metadata node. Thanks for any information!
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