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What distinguishes the train dataset from the validation dataset in machine learning model development?
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How does nested cross-validation differ from traditional cross-validation techniques?
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Why is nested cross-validation considered more reliable for model evaluation?
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What is data leakage with respect to feature engineering/transformation in machine learning?
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Why is it important to preserve class ratios during data splitting?
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What can happen if time-dependent records are not preserved during random splitting of data?
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How can data leakage be prevented during feature engineering/transformation?
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What is the primary purpose of preserving class ratios during data splitting?
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What is the primary purpose of the Receiver Operating Characteristic (ROC) curve?
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