Stop Tuning Hyperparameters. Start Tuning Your Problem.

Many machine learning projects fail not from inadequate models, but from incorrectly framing the business problem from the very beginning. Data scientists often fall into the "productive procrastination" trap of hyperparameter tuning because it offers fast, measurable feedback, unlike the slower, more ambiguous work of problem definition. This focus on optimizing models for the wrong problem leads to catastrophic failures, such as Zillow losing $500 million by failing to question its core assumptions. To prevent this, teams should prioritize a rigorous problem-framing protocol to define the specific business decision and potential costs of errors before ever writing training code. This shift ensures that the immense effort of model building is directed at solving a problem that truly matters.