Explain overfitting, underfitting, and regularization

Q: Explain overfitting, underfitting, and regularization

This question evaluates understanding of model generalization, overfitting versus underfitting, the bias–variance tradeoff, and regularization techniques within the Machine Learning domain, testing competency in interpreting training versus validation/test performance.

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You are asked ML fundamentals questions.

What are overfitting and underfitting ? Describe how they show up in training vs. validation/test performance.
Explain the bias–variance tradeoff and how it relates to over/underfitting.
What is regularization ? Compare L1 and L2 regularization:
- effect on weights
- effect on sparsity / feature selection
- when you might prefer one over the other
What is dropout ? Why can it reduce overfitting, and what are common pitfalls when using it?
List a few additional techniques to reduce overfitting (besides L1/L2/dropout) and when you would use them.

Explain overfitting, underfitting, and regularization

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