Addressing class imbalance in supervised learning
Class imbalance is a frequent issue in predictive modelling. Explain practical approaches for data scientists: resampling, class-weighting, threshold tuning and appropriate metrics. Indicate when to prefer each option, how to validate choices via cross-validation, and how to communicate trade-offs to stakeholders.
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