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Nandish Jayaram commented on MADLIB-1222: ----------------------------------------- Example use case, and handling it: 1) User encodes dep vars with whatever tool they want and put it in the column `color` . Maybe they do this to anonymize, maybe the data is just in that format already: {code} blue [1,0,0] red [0,1,0] green [0,0,1] {code} --------------- start MADlib 2) runs mini-batch preprocess (if planning to use mini-batch) 3) runs MLP classification train (IGD or mini-batch) 4a) runs MLP predict (response): {code} actual predicted [0,1,0] [1,0,0] [0,0,1] [0,0,1] [1,0,0] [1,0,0] etc. {code} 4b) runs MLP predict (prob): {code} actual estimated_prob [0,1,0] [0.85, 0.10, ,0.05] [0,0,1] [0.0 , 0.1 , 0.9] [1,0,0] [0.75, 0.20, 0.05] etc. {code} end MADlib -------------- 5) User maps back to red, blue, green since they know the mapping but MADlib doesn't. > Support already encoded arrays for dependent var in MLP classification > ---------------------------------------------------------------------- > > Key: MADLIB-1222 > URL: https://issues.apache.org/jira/browse/MADLIB-1222 > Project: Apache MADlib > Issue Type: New Feature > Components: Module: Neural Networks > Reporter: Nandish Jayaram > Priority: Major > Fix For: v1.14 > > > MLP currently only supports scalar dependent variables for MLP > classification. If a user has already one-hot encoded categorical variables > the dependent variable will be an array, and hence unusable with > mlp_classification. This feature request is to allow the use of one-hot > encoded array for dependent vars in MLP classification. -- This message was sent by Atlassian JIRA (v7.6.3#76005)