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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In my experience, the most challenging aspect is often finding the sweet spot between model complexity and overfitting, particularly when dealing with imbalanced datasets. It's not just about data preprocessing or feature selection, but also about understanding the underlying relationships between variables and making informed decisions about regularization techniques, hyperparameter tuning, and ensemble methods.

There are always too many factors to be considered. Hyper parameter tuning is a real hassle. I personally find it more challenging.