And...just as I wrote that I received more info from R&D: 1. You need to use the “Decision node”(not Decision Tree Node) after sampling node, you can specify the adjusted prior as your original prior (before sampling) and you will keep your data prior from the oversampling. If you don't use the decision node and you specify your adjusted prior as the original prior in input data source node, there will not be any predicted probability adjustment by the prior because the ratio is always 1. Decision matrix is related to calculating profit and loss, it will be applied separately after the prior adjustment. 2. For rare event modeling, usually an oversampling is required, it is not necessary to make the sample balanced. However it depends on your data and analysis. 3. take a look at the Proc Arbor procedure document, it has the details. The proc option “DECSEARCH” is for "Use Decisions" The proc option “PRIORSSEARCH” is for "Use Priors" in "Split Search 4. The Cutoff node will not impact on the decision tree node itself. The cutoff node will create just the EM_CUTOFF variable, which is the classification variable resulted by the new cutoff value. For exmple, your new cutoff is 0.06, a piece of cutoff score code will be added to the end of the previous score code. IF P_good_badgood > 0.06 THEN EM_CUTOFF = 1; ELSE EM_CUTOFF = 0;
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