entropy
AI, ML & GenAI interview questions tagged entropy, across every topic.
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Concepts behind "entropy"
The curriculum that explains the ideas these questions test.
Foundational
Decision Trees and Splitting CriteriaA decision tree recursively splits the feature space by choosing the split that most reduces impurity (Gini or entropy), producing a flowchart you can read top to bottom. Interviews probe trees because they reveal whether you understand impurity-based splitting, why depth is the bias-variance knob, and how a single high-variance tree becomes the building block for random forests and gradient boosting.📊 Evaluation & ML Foundations
Core
Information Theory for MLML rests on four information-theoretic quantities: entropy (how uncertain a distribution is), cross-entropy (the cost of modeling the true distribution with your predicted one, the classification loss), KL divergence (the gap between two distributions), and mutual information (how much one variable reveals about another). You meet them as the loss you minimize, the regularizer inside VAEs and RLHF, and the split criterion in decision trees. AI, ML, and GenAI engineer interviews test this because cross-entropy and KL sit under training, distillation, and alignment.📊 Evaluation & ML FoundationsSign in