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.

HardTechnical

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Describe methods to calibrate probabilistic outputs of classifiers: Platt scaling, isotonic regression, and temperature scaling for neural nets. Given non-stationary data that drifts over time, propose a deployable strategy to maintain well-calibrated probabilities in production.

MediumTechnical

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Implement a simple random-search hyperparameter tuner in Python. The function should accept a callable that builds an sklearn-like estimator given a parameter dict, parameter distributions (lists or distributions), training data X,y, number of iterations n_iter, cross-validation folds, and a scoring function, and return the best parameters and score. Focus on correctness and reproducibility.

MediumTechnical

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You have limited compute budget and need to tune XGBoost on 10M rows. Propose a practical hyperparameter tuning strategy that balances search quality and cost, including choices like smaller proxy datasets, early stopping, multi-fidelity optimization (successive halving), and warm-starting.

MediumTechnical

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Discuss strategies for encoding high-cardinality categorical features when training (a) tree-based models and (b) linear models / neural nets. Cover one-hot, target/mean encoding with leakage prevention, hashing trick, learned embeddings, and pitfalls such as target leakage and overfitting.

HardSystem Design

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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.

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