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Model evaluation and validation: Machine Learning course | Zoonk

15. Model evaluation and validation

Separate training, validation, and test data; choose metrics that match the real cost of errors; and compare against meaningful baselines. Use cross-validation, confidence intervals, and error analysis to judge whether gains are reliable.

Machine Learning

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