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Random forest is a machine-learning method based on combining the outputs of many decision trees.
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Does oversampling/undersampling change the distribution of the data?
I have an imbalanced dataset (10000 positives and 300 negatives) and have divided this into train and test sets. I perform oversampling/undersampling only on the train set since doing this on the test …