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If you have discrete variables (which is generally considered bad practice with SMOTE, I guess for exactly this reason), this can happen quite easily. Suppose you have a binary variable. Then some of the synthetic data points are likely to have fractional values for that variable, and a tree can separate those samples out with two splits. Categorical ...


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I think you're confusing some notions here - no pun intended with your pseudo. Adaboost is a boosting algorithm that aggregate several "weak learner" to make more robust predictions. Thus it is a meta algorithm so there are two level of optimisation. Reduce the error over the meta model (aggregation of weak learners) => the weighted error rate intervenes ...


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