How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

What difficulty level is this interview question?

This is a medium difficulty Machine Learning question, commonly asked during Onsite rounds at Uber.

What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Uber during technical interviews.

Explain XGBoost depth, regularization, and dropout

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of model complexity and regularization across gradient-boosted decision trees and neural networks—covering how tree depth impacts bias/variance and computational cost, how L1/L2 and weight decay modify objectives and learned parameters, dropout behavior at inference, and distinctions between training and inference phases in Machine Learning. It is commonly asked to assess reasoning about overfitting, generalization, computational and deployment trade-offs, testing domain knowledge in supervised learning and regularization, and requires primarily conceptual understanding with practical-application considerations.

Uber

Sep 6, 2025, 12:00 AM

Machine Learning Engineer

Onsite

Machine Learning

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?

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

Solution

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