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I would like to classify a dataset Credit Scoring, which is composed of 21 attributes, some of them are numeric and others are boolean.

For the output, I want to know if they have a good or bad credit based on those attributes, without calculating any numeric value for the credit score.

I am using Weka for this task. However, I am not sure what are the best/ideal classifiers for that kind of datasets.

Anyone here can put me in the right direction?

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Well, it depends on the structure of the data. The best way is to try all the intelligent models like naive bayes, random forest, svm with different parameters by grid search. There is no model works best all the time for classification. However, neural network (named Multilayer Perceptron on weka) is supposed to be better if it is set correctly.

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