The most important decision in an ML system isn’t the model or the tuning. It’s how you define the problem.

A well-trained model solving the wrong problem will confidently optimize for the wrong outcome. A simpler model, aligned with the real goal, will deliver more value.

Getting the problem framing right matters more than anything that comes after.