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Topic: Text analysis
Hey guys,

We want to do a binary classification on a text data set with the distribution 80% negative class, 20% positive class. In order to reach maximum statistical meaningfulness, we want to do so by using 10-fold cross validation.

If we model this within Rapidminer, we are unsuccessful since it doesn’t output any statistical metrics (like precision, recall, etc).


We found a workaround that works, but it doesn’t make any sense out of a ML perspective: If we first divide into training or test and then use 10-fold-crossvalidation it works — But the training or test split should be part of the crossvaligdation (9 training folds, 1 test fold, 10 iterations). So right now the only way to get this working is by FIRST dividing into test and training and THEN use X-Validation. Did we model it the right way or did we miss anything?


If you need any more information for helping us, just comment.

Best regards!

by Thiemo Thu Dec 01 11:22:50 UTC 2016
 
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