Explain XGBoost depth, regularization, and dropout

Q: Explain XGBoost depth, regularization, and dropout

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Question

ML Conceptual Questions (Onsite)

Answer the following:

(a) Gradient-boosted decision trees: How does maximum tree depth affect bias/variance, overfitting risk, and training/inference cost? How would you choose it in practice?

(b) Neural networks: Compare L1 vs L2 regularization and weight decay — how do they modify the objective, gradients, and learned parameters?

(c) Dropout: After applying dropout during training, what should happen at inference time, and why?

(d) Training vs inference: Define and contrast these phases for ML models, including data flows, randomness, and performance considerations.

Explain XGBoost depth, regularization, and dropout

ML Conceptual Questions (Onsite)

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