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During which phase of CRISP-DM are the business goals converted into data science problem definitions?

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What does the term "interpretability" refer to in the context of machine learning models?

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How does understanding a model increase trust in its predictions?

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Define scalability in the context of deploying machine learning models.

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What is the purpose of ongoing tuning in production environments?

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In what scenarios might the deployment phase be as simple as generating a report?

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Why is model evaluation alone not sufficient in data science?

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What considerations are involved in assessing the fixed costs of a campaign and model?

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What does the term "overfitting" mean in the context of machine learning models?

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Why should data science not be treated as a black box?

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