Understand Bias-Variance Trade-off and Regularization Techniques

Q: Understand Bias-Variance Trade-off and Regularization Techniques

This question evaluates a candidate's mastery of core supervised learning concepts—bias–variance trade-off, overfitting versus underfitting and their mitigation, regularization (L1 vs L2), k-fold cross-validation, and linear regression assumptions—with emphasis on model generalization, evaluation, and statistical foundations.

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Question

Rapid-Fire ML Fundamentals — Core Concepts

Context

You are in a rapid-fire onsite session with a CIO focused on machine-learning fundamentals for a Data Scientist role. Keep answers concise and use equations where helpful.

Questions

Explain the bias–variance trade-off.
Define overfitting and underfitting, and give prevention techniques.
What is regularization, and how do L1 and L2 differ?
Describe k-fold cross-validation and why it is useful.
List key assumptions behind linear regression.

Hint

Be concise; include equations where appropriate.

Understand Bias-Variance Trade-off and Regularization Techniques

Rapid-Fire ML Fundamentals — Core Concepts

Context

Questions

Hint

Solution

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