Machine Learning Algorithms and Theory Questions

Core supervised and unsupervised machine learning algorithms and the theoretical principles that guide their selection and use. Covers linear regression, logistic regression, decision trees, random forests, gradient boosting, support vector machines, k means clustering, hierarchical clustering, principal component analysis, and anomaly detection. Topics include model selection, bias variance trade off, regularization, overfitting and underfitting, ensemble methods and why they reduce variance, computational complexity and scaling considerations, interpretability versus predictive power, common hyperparameters and tuning strategies, and practical guidance on when each algorithm is appropriate given data size, feature types, noise, and explainability requirements.

MediumTechnical

22 practiced

Explain why bagging (e.g., random forest) tends to reduce variance whereas boosting (e.g., gradient boosting) focuses on reducing bias. Provide a concise mathematical intuition and discuss practical implications in production, including latency, model complexity, and risk of overfitting.

HardSystem Design

24 practiced

Design a distributed training and feature engineering pipeline to train gradient-boosted models on 100M+ rows and 10k features. Discuss where data should live (file formats, storage), feature engineering at scale (Spark/Dask), use of a feature store, cross-validation strategies, distributed hyperparameter tuning, reproducibility, and model artifact management.

EasyTechnical

29 practiced

Why is diversity among base learners important for ensemble performance? Give mechanisms to increase diversity (data sampling, feature subspace sampling, different model families) and describe metrics that quantify ensemble diversity. How does diversity relate to ensemble error decomposition?

MediumTechnical

25 practiced

You are given 1M training rows, 1000 features including many high-cardinality categorical features and moderate label noise. Which candidate algorithms would you prototype first and why? Provide a prioritized plan that balances predictive power, training time, interpretability, and ease of deployment for production.

HardTechnical

42 practiced

Provide a mathematically grounded explanation of gradient boosting: how boosting can be seen as gradient descent in function space, how the negative gradient corresponds to residuals, and how choice of loss function (squared error vs logistic) affects the updates. Explain common regularization techniques in boosting and their theoretical effect.

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