Bias-Variance Tradeoff

Question: Why does a model that performs perfectly on training data often fail in production? Key points: This is the bias-variance tradeoff. High variance/overfitting means the model has memorized noise/idiosyncrasies of the training set rather than learning generalizable patterns, so it fails on unseen data. High bias/underfitting means the model is too simple to capture the underlying signal at all. Good generalization requires balancing model complexity against the amount/quality of training data, typically via regularization, cross-validation, and appropriately sized/complex models.