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Create a set of evaluation metrics for a machine learning model, focusing on aspects such as accuracy, precision, recall, F1 score, and AUC-ROC. Include steps for performing a train-test split and cross-validation to ensure robust model performance assessment. Address how to interpret these metrics and use them to compare different models.
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