Explain bias-variance and evaluate a classifier

Q: Explain bias-variance and evaluate a classifier

This is a Machine Learning interview question from Microsoft for Machine Learning Engineer roles. View the full question and solution on PracHub.

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Question

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).

Explain bias-variance and evaluate a classifier

1) Bias–variance

2) Classification metrics

3) Confidence intervals

Solution

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