Re: Sampling bias and class imbalance for target variable
Thanks for your feedback.
Earlier in the lectures we learned about penalizing losses differently by using a loss matrix. Is this one instance where this technique can be useful, by penalizing the case "classifier predicts false when the target is true (fraud)" more severe than the other error type (for a binary target)?
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