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Explain Overfitting and Underfitting in Machine Learning

AmazonData Scientist · Technical Screen
MediumData ScientistTechnical ScreenMachine Learning

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This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Explain Overfitting and Underfitting in Machine Learning states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Scenario

Interview on classical machine-learning fundamentals and computer-vision–related techniques.

Question

Differentiate overfitting and underfitting. How do you detect and mitigate each? What is data augmentation? Provide image-specific examples. Describe the main components and purposes of a Convolutional Neural Network. How does an RNN process sequential data? Detail the roles of positional embeddings, self-attention, residual connections and feed-forward networks in a transformer encoder. What is dropout and why does it help? Compare bagging and boosting in terms of bias, variance and algorithm behavior.

Hints

Define concepts, mention bias-variance trade-off, regularization tricks, architectures, and practical diagnostics.