entropy
Applied AI 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 picking the split that most reduces impurity (Gini or entropy), producing a flowchart you can read top to bottom. Interviews probe trees because they expose whether you understand impurity-based splitting, why depth controls 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 MLInformation theory gives ML its core measures: entropy (uncertainty in a distribution), cross-entropy (the cost of modeling the true distribution with your predicted one, the classification loss), KL divergence (how far one distribution is from another), and mutual information (how much one variable tells you about another). These appear as the loss you minimize, the regularizer in VAEs and RLHF, and the splitting criterion in decision trees. Applied-AI interviews probe it because cross-entropy and KL underlie training, distillation, and alignment.📊 Evaluation & ML FoundationsSign in