Explain overfitting, regularization, and LLM techniques

You’re in an ML interview. Answer the following conceptual questions clearly and concisely, using examples where helpful: ## 1) Model fit - What is **overfitting** vs **underfitting**? - For each, list common symptoms you would see in training/validation curves. - Give 3–5 practical ways to mitigate each problem. ## 2) Regularization - Compare **L1** vs **L2** regularization: - objective/penalty form - effect on weights (sparsity vs shrinkage) - when you would prefer one over the other - interaction with correlated features ## 3) LLM-related topics Explain the purpose, core idea, and major trade-offs for: - **LoRA (low-rank adaptation)** for fine-tuning - **RAG (retrieval-augmented generation)** - **Agents** (tool-use / planning loops) For each, describe: - what problem it solves - what data it needs - what can go wrong (failure modes) - how you would evaluate it in production ## 4) Project deep dive (CV example) Pick one computer-vision project you’ve worked on (e.g., classification/detection/segmentation) and be prepared to explain: - problem statement and business goal - dataset construction/labeling and leakage risks - model choice and baseline - training details (augmentation, loss, class imbalance, hyperparameters) - evaluation metrics and thresholding - key errors you found and how you fixed them - how you would deploy/monitor it (latency, drift, feedback loop)