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I am trying to train a Random Forest classifier on a binary classification problem but with a highly imbalanced dataset where the positive class is much smaller than the negative class . I have undersampled my dataset so that both classes have equal proportions and did 10 fold cross validation over this dataset.
However I am having bad results (averaged over the 10 folds):

  • recall : 46%
  • precision : 54%
  • accuracy : 53%

I was wondering if oversampling the positive class would yield better results. What would be the benefits of undersamling versus oversampling?

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  • $\begingroup$ Did you have a look at the confusion matrix of your model's predictions? $\endgroup$ – deemel Jun 15 '18 at 20:54
  • $\begingroup$ How many training samples do you have after undersampling? $\endgroup$ – jonnor Jun 16 '18 at 23:11

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