Explain bias-variance and evaluate a classifier

You are interviewing for an Applied Scientist internship. Answer the following ML foundations questions. ## 1) Bias–variance - Define **bias** and **variance** in supervised learning. - Explain the **bias–variance tradeoff** and how it relates to **underfitting vs. overfitting**. - Give 2–3 practical ways to reduce: - high bias - high variance ## 2) Classification metrics - Define **accuracy, precision, recall, F1**. - Explain when accuracy is misleading. - Given a confusion matrix (TP, FP, TN, FN), show how you would compute the metrics and choose which one to optimize for an imbalanced problem. ## 3) Confidence intervals - What is a **confidence interval (CI)**? - Suppose you evaluated a binary classifier on a test set of size \(n\) and observed accuracy \(\hat{p}\). Describe how you would compute a **95% CI** for the true accuracy and what assumptions are required. - Name at least one alternative method to build a CI if assumptions are weak (e.g., small sample size or correlated examples).