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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For me, it's been a delicate trade-off between overfitting and underfitting. Achieving optimal regularization and hyperparameter tuning is crucial, but often the most challenging aspect lies in selecting the right model architecture and complexity to capture the underlying patterns in the data without over-specializing to the training set.