Hi Alice, It seems to me you are trying to use the Decisions Node to train models with an oversampled training set (to address your over represented respondents) and then get the predicted probabilities adjusted using the decision weights. Your example looks very similar to the one described on Applied Analytics Using Enterprise Miner course notes, specifically on the Enrollment Management Case Study (Appendix A). Try this on your Decisions node: 1. Go to the Decision Weights table. 2. For level 1 specify Decision 1 as 28.49 (1/0.0351=28.49) 3. For level 0 specify Decision 2 as 10.36 (1/.9649=10.36) 4. Leave all the other decisions for the remaining levels as 0. From the brief explanation on the course notes, what this does is to calculate a frequency variable for each observation based on the level you specified. This frequency will weight your resulting model appropriately. Please feel free to follow up if I am missing anything on your modeling task. Thanks! Miguel ... View more

Hi Rakkesh, It seems to me that the Decision Tree node works like you intended (it opens with only the root node on interactive mode if you haven't run it). Try adding a brand new Decision Tree node, connect your data to the decision tree, and then click the ellipsis next to "Interactive" on the train properties. Notice that all it takes for your tree node is to update the path, which does automatically, and opens the interactive tree window with just the root node. I think that if you add the run, and run it, then you will see a big tree when you open the interactive mode. But if you just add, connect, and click on interactive, it should open with the root node only. I just tried with my EM 7.1 version and it worked as described. Please confirm it worked for you! Thanks, Miguel ... View more