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Answers
unfortunately rapid miner does not provide this two performance measures, since they do not measure the classification performance directly, rather measuring the correctness of the learner's assumption. Which is to assume that the data is normal distributed in case of the logistic regression, if I remember correctly. But even if an assumption is violated, the performance might be good. Otherwise algorithms like NaiveBayes (independence assumption of attributes) would not be that successfull in practice.
In the data mining community the most widely used method for performance estimation is crossvalidation. It measures the performance directly on the data and hence copes with cases, where data does not fit into any of the (simple) distribution assuptions or learner just do not work on continuous distribution as the powerfull svms.
Greetings,
Sebastian