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Have you ever trained a model that seemed perfect during development but performed poorly in real-life scenarios, especially when dealing with rare events or classes? This is a common pitfall when working with imbalanced datasets, a prevalent issue i...

Data is always dynamic, existing in different forms. Real-world scenarios are continuously changing, thus shifting the data that feeds into machine learning models, leading to what is known as data distribution shifts. These shifts can significantly ...
