Model Selection, Tuning, and Generalization Questions

Choosing and tuning models so they generalize to unseen data rather than memorizing the training set. Covers the bias-variance tradeoff and its decomposition, diagnosing over- and under-fitting from learning curves, and regularization techniques such as L1/L2 penalties, dropout, and early stopping, alongside cross-validation strategies and grid, random, and Bayesian hyperparameter search. Emphasizes a principled, reproducible process for selecting model complexity and tuning against a real compute-versus-accuracy budget rather than ad-hoc trial and error.