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I’m dealing with a highly unbalanced dataset where 20% of data belongs to class A and 80% belongs to class B.
It’s very hard for us to produce synthetic class A data.
Just wondering if the below approach is a sensible thing to do:
Total data points: 100
Class A : 20
Class B : 80
How about splitting the dataset into 4 separate samples consisting of 20 A’s and 20 B’s. In other words, I’m mixing the 20 A’s with different samples of 20 B’s. We’d have 4 models (say random forest or so) and finally the decision is taken from what the majority of these 4 models predict?