Choose clustering vs regression; explain KNN
Overview
This question evaluates model selection between clustering and regression given partially labeled outcomes, familiarity with clustering algorithms (K-Means, Hierarchical/Agglomerative, DBSCAN/HDBSCAN, Gaussian Mixture Models), K-Nearest Neighbors behavior, evaluation metrics, hyperparameters, and deployment considerations for a Data Scientist role in Machine Learning. It is commonly asked to assess judgment on supervised versus unsupervised approaches, trade-offs driven by label availability, objective and cost of errors, algorithmic assumptions, scalability, and practical deployment techniques, and the level spans both conceptual understanding and practical application.
When would you use clustering vs. regression on a business problem with partially labeled outcomes? Specify the decision criteria (label availability, objective, evaluation metrics, cost of errors). Enumerate at least four clustering algorithms (K-Means, Hierarchical/Agglomerative, DBSCAN/HDBSCAN, Gaussian Mixture Models) and compare assumptions, key hyperparameters, scalability, distance metrics, and failure modes (e.g., non-spherical clusters, varying density, high-dimensional sparsity, mixed data types). Give concrete scenarios selecting DBSCAN over K-Means and vice versa. Finally, explain K-Nearest Neighbors to a non-technical stakeholder with a real-world analogy, then deepen: choosing k, weighting by distance, effects of feature scaling, curse of dimensionality, and how to deploy KNN efficiently (KD-tree/ball-tree, approximate neighbors).