How do you design evaluation frameworks for ML systems where ground truth is delayed, expensive to obtain, or fundamentally subjective? Provide specific strategies for each case.

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Evaluation in imperfect label conditions requires careful methodology design for each scenario. For delayed ground truth, such as predicting customer churn months in advance, I use several strategies.