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I am trying to solve an imbalanced binary classification problem using a model to predict the minority class (stroke victims). I used oversampling on the training data to make synthetic instances of stroke cases so that I could address the data imbalance issue. However, I kept the test data as its normal imbalanced…
How do I split the data into training, validation and testing subsets? (Not just training and testing)