For an imbalanced dataset, can we penalize incorrect predictions where the correct class is the minority class … in roughly the same proportion as there is between the minority and majority class in the dataset?
For example, if the minority class = 1 and 95% of the dataset is the majority class (class = 0), is there a way to say, “If the actual is 1 and you predicted 0, then increase the penalty by penalty * X”?