Hi Jon, Maybe I am overthinking what you mean by "rules for the final model by majority vote". Where did you get that idea or definition? As far as I understand there is no voting of the rules on a bagging model, but on the predicted probabilities of each of those models. In your specific example, every observation in your data set will be scored with each of one of the trees in your bagged model. The predicted probabilities of these models are then averaged. Below a diagram to illustrate what happens behind the scenes (I grabbed it from the paper I mentioned). The actual code in the Start Groups and End Groups nodes is quite more efficient. Using this alternative diagram you could switch the option in the Ensemble node from averaging to vote. But again, you are voting on the predicted probabilities of each of the models for an observation, not voting on the rules of the models. Whether you combine the predicted probabilities of the models by averaging or by voting, it is the predicted (posterior) probabilities, not the rules. When you have data partition, your models are assessed using a statistic of your validation set. For the specific case of trees, the pruning of the tree model will be based on the validation set. Are you sure it is not the rules of each model that you want to see? For example, in the figure I pasted you have 4 Decision Trees. You could learn more details of your model by looking at the rules of each tree if you think that adds value, but you cannot combine them into "rules of the final model". Does this help at all? Miguel
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