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From a certain point of view, optimizing the meta- or hyperparameter is the opposite approach to ensemble models. They are good for sifferent situations. The point of ensemble models (stacked or bagged) is to reduce variance in the submodels due to the imperfect training (e.g. too few training cases). Bagging does not change the prediction and thus does not ...


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A potentially bigger problem than data leakage between test and training sets is the unreliability of test/train splits of small data sets. You need many thousands of cases for that to be reliable. Otherwise you are throwing away information by limiting the size of the training set, and you are getting imprecise estimates of model validity by having too ...


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