Hi I am using Decision Tree model to solve a classification problem. My Target variable is highly imbalanced.i.e.  60212 (99%) Non-event and 633 (1%) event. I using sampling to balance the class of target variable as shown in the picture below. In addition to using cross-validation to tune the model I am also using different sampling ratios to check which ratio will work best for me.   After sampling I am using inverse prior probabilities to adjust decision weights. This was suggested in this post on SAS forums. My question here is that which "inverse prior probabilities" shall i use: the original data before sampling & partitioning or the data from output of sample node or the data from output of partition node (train data)   If I use the "Decision Processing" Node after partitioning it allows me to use "Inverse Prior Weights".      When I click this, the weights are automatically adjusted as shown the screenshot below.   Based on my calculations (below) these are the weights from the "Training Data" coming out of partitioning node.  Is this correct? Shall I override these with Inv priors from my original un-sampled data shown in row 1 below (i.e. 1.01051 and 96.12164)       ... View more

Below my diagram where I am fitting tree based models. The data node labeled "cfgus_data" is used for training and validation. My testing data is separate so I added it as node labeled "scoring data" I have also set the output type property to Data and role to test for this data set under "Train" section of properties.   What I wanted to get was the "classification table" showing me TP, FP, TN, FN on test data. However, in the "Output" section of results I don't see this for test data set. I am sharing the "Event Classification Table" section below for reference which is only showing the metrics for Train and Validate part.   What I am doing wrong? Please advise me how to fix this.. Event Classification Table Model Selection based on Valid: Average Profit (_VAPROF_) Model Model Data Target False True False True Node Description Role Target Label Negative Negative Positive Positive HPTree HP Tree TRAIN FG 51 455 12 31 HPTree HP Tree VALIDATE FG 20 193 8 16 Tree Decision Tree TRAIN FG 82 467 0 0 Tree Decision Tree VALIDATE FG 36 201 0 0       ... View more