Explain bias–variance, overfitting, and vanishing gradients

Q: Explain bias–variance, overfitting, and vanishing gradients

This question evaluates understanding of core machine learning fundamentals—specifically the bias–variance tradeoff, overfitting detection and mitigation, and the vanishing gradient phenomenon in deep networks—testing competencies in model generalization, diagnostic reasoning, and training dynamics.

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Answer the following ML fundamentals questions:

Bias–variance tradeoff: What are bias and variance? How do they relate to underfitting/overfitting? What practical actions increase/decrease each?
Overfitting: What does it mean for a model to overfit? How would you detect it, and what are common mitigation strategies?
Vanishing gradients: What is the vanishing gradient problem? When does it occur (e.g., deep nets / RNNs), what are its symptoms, and what techniques mitigate it?

Explain bias–variance, overfitting, and vanishing gradients

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