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There is no universal minimum. The following planning guide provides practical starting ranges, not guarantees. The correct threshold depends on the complexity of the decision, the number of outcome classes, and the cost of a wrong prediction.

Limited data is not a reason to stop. It is a reason to choose a narrower objective and apply stronger controls.

Small datasets require time-aware testing. Randomly splitting records can leak future information into the training set, especially when the same customer appears many times or when records contain later updates.