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No, you want your model to be well calibrated. If the model accurately assessed a 0.55 probability of going blue, then that is what you want.

People who try to correct for “unbalanced classes” and contort their model to give polarizing predictions are frankly being pretty dumb.

The correct answer is to take your well calibrated probabilities and use you brain on what to do with them.

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This is not a matter of class balance that much. If you want to predict which of two parties somebody will vote with, the most natural framing is that of binary classification.

For that you need to threshold your predictions. Ideally you'd like your model to generate a bimodal distribution so that you can threshold without many false positives etc.

Yes but the prompt here states that all we know is the probability is either 0.55 or 0.45. By definition this is the best model you can produce.

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