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

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Explain the kernel trick in support vector machines: how kernel functions implicitly map inputs to high-dimensional feature spaces via inner products. List common kernels (linear, polynomial, RBF), explain when non-linear kernels are beneficial and discuss computational trade-offs for large datasets.

EasyTechnical

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You have ~10,000 labeled examples and 20 features (mix of categorical and numeric). Regulators require model explanations. Compare logistic regression and a shallow decision tree for this binary classification problem. Which would you choose and why? Include data preparation and pros/cons relative to interpretability and predictive power.

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You must build a predictive system that consumes numerical, categorical, text, and image data and must be explainable to product managers. Propose a modular pipeline design that balances predictive performance and interpretability: per-modality models, feature fusion strategies, modality-specific explainers (SHAP for tabular, Grad-CAM for images), and a UI/reporting approach to present explanations across modalities.

EasyTechnical

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Define supervised, unsupervised, and semi-supervised learning with practical business examples for each. For every example, state the types of models you might use and the primary objective (prediction, grouping, representation).

MediumTechnical

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You have unlabeled clustering results. Describe a practical plan to evaluate clustering quality without ground truth. Include internal metrics (silhouette score, Davies–Bouldin), stability and robustness checks (subsampling, bootstrapping), visual diagnostics, and validating clusters via downstream supervised tasks or business KPIs.

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