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Core
Model Debugging Methodology
Model debugging is the systematic process of root-causing why a model underperforms: deciding whether the cause is the data, the features, the labels, model capacity, or the evaluation itself, rather than blindly tuning hyperparameters. The method leans on error analysis over slices and the train/val/test gap ladder to localize the failure before fixing it. Applied AI interviews probe it because most candidates jump to bigger models or more tuning when the real bug is a leaky feature, a noisy label set, or a broken eval.
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