I knew the definitions but stumbled when they pushed on the 'which models and why' part.
Start by clearly defining overfitting and underfitting, then explain the bias-variance tradeoff as the underlying concept. For each, describe which model types are most prone (e.g., high-capacity models for overfitting, high-bias models for underfitting) and why, using concrete examples. Finally, mention techniques to detect and mitigate each, tying back to practical ML engineering.
Pro tip: Emphasize that the goal is not to eliminate overfitting entirely but to manage the bias-variance tradeoff for optimal generalization. Relate it to NVIDIA's focus on scalable AI by mentioning how model complexity and data size interact in real-world deployments.
Explain overfitting as when a model learns noise in training data and fails to generalize, and underfitting as when a model is too simple to capture underlying patterns, performing poorly on both training and test data.
Describe how overfitting corresponds to high variance and low bias, while underfitting corresponds to high bias and low variance. This provides a theoretical foundation for understanding model behavior.
For overfitting, mention complex models like deep neural networks, decision trees, and k-NN with small k. For underfitting, mention simple models like linear regression, high-bias models, or overly regularized models.
Overfitting occurs in high-capacity models because they can memorize training data, including noise. Underfitting occurs in low-capacity models because they lack the flexibility to capture complex relationships.
Mention techniques like cross-validation, learning curves, regularization (L1/L2, dropout), early stopping, data augmentation, and ensemble methods to address overfitting; and increasing model complexity, adding features, or reducing regularization for underfitting.
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