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.

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.

HardTechnical

23 practiced

Case study: 1M rows, 10k sparse TF-IDF text features, binary label. Requirements: train in under 2 hours, serve predictions <50ms, and provide interpretable insights into model predictions. Propose an end-to-end solution: feature selection/dimensionality reduction, model choice, training infra, inference architecture, and explainability methods suitable for sparse text features.

HardTechnical

21 practiced

Compare computational complexity and practical training cost of Random Forests versus Gradient Boosting (e.g., XGBoost) on large tabular datasets. Discuss per-tree cost, parallelization strategies (across trees vs within tree), typical number of trees needed for comparable performance, and implications for distributed training and resource provisioning.

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.

EasyTechnical

42 practiced

Explain how feature scaling (standardization, normalization, min-max) affects algorithms that rely on distances or gradients such as SVM and k-means. Provide concrete failure modes when features are on very different scales and recommend practical scaling pipelines for mixed feature types.

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