When developing machine learning models, what’s the most challenging aspect you’ve faced in balancing accuracy with generalization? Is it data preprocessing, feature selection, or something deeper in the model architecture? Just curious.

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Achieving the right balance between accuracy and generalization is a complex task in machine learning. Apart from data preprocessing and feature selection, the model architecture plays a crucial role. It's essential to strike a balance to ensure optimal performance across different datasets.