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The leave-batch-out design is an experimental protocol for evaluating a model's out-of-distribution generalization ability: certain experimental batches or data sources are completely excluded from the training set, the model is trained only on the remaining batches, and then tested on the held-out batches.
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Long-term
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10/22/2026
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