pruning
AI, ML & GenAI interview questions tagged pruning, across every topic.
3 questions · 1 unlocked for you
Concepts behind "pruning"
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
BacktrackingBacktracking is systematic search across a tree of partial solutions: at each step you pick an option, explore deeper, and undo the pick before trying the next (choose, explore, unchoose). Pruning cuts branches that cannot reach a valid solution before you spend work on them. Interviews test it because permutations, combinations, subsets, and constraint problems (N-queens, sudoku) all share this template, and the in-place choose/unchoose pattern avoids re-allocating state at every node, which is the difference between an elegant solution and an exponential memory blowup.💻 Coding & Engineering CraftSign in