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

28 practiced

You have a small labeled dataset (~10k examples) with many categorical features, some features with >10k unique values. Propose feature engineering approaches, model choices, and regularization strategies to avoid overfitting while retaining interpretability, considering memory and latency constraints in production.

HardTechnical

25 practiced

You receive a dataset with thousands of categorical features, significant missingness, and variable label delay. Propose a full production pipeline: preprocessing steps (imputation, encoding), feature selection at scale, model selection, offline and online evaluation metrics, strategies to prevent leakage (especially with target encoding), and how to prioritize features for labeling/collection.

EasyTechnical

24 practiced

Compare k-fold cross-validation with a single holdout validation set. Discuss bias and variance of the performance estimate, computational cost, and scenarios where nested cross-validation is required for hyperparameter selection in applied projects.

MediumTechnical

30 practiced

Implement k-means clustering from scratch in Python using numpy. Requirements:- Function: kmeans(X, k, max_iters=100, tol=1e-4, init='kmeans++'|'random')- Return centroids, labels, and inertia (sum of squared distances)- Implement k-means++ initialization option- Handle empty clusters gracefully- Vectorize distance computations where possible

HardTechnical

26 practiced

Analyze sample complexity for learning a linear classifier. Discuss how margin size, presence of label noise, and model capacity (VC dimension) affect the number of labeled examples required to reach a target generalization error. Provide intuitive derivations and practical guidance for data collection strategies.

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