Science-breadth interview for an Amazon Applied Scientist role, assessing foundational understanding of common machine-learning algorithms.

a) Explain in detail how a decision-tree model is trained: how split points are chosen, how stopping criteria/pruning work, and how overfitting is avoided. b) Name at least three clustering algorithms and describe the core principle behind each one.

Cover impurity measures (Gini/entropy), information gain, pre-pruning vs post-pruning; for clustering mention K-Means, DBSCAN, Hierarchical, Gaussian Mixture, etc., and highlight distance, density or probabilistic assumptions.

Amazon Data Scientist interview question: Explain Decision-Tree Training and Clustering Algorithms. {"blocks": [{"key": "54c33350", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {...