Trees, Bagging & Random Forests – Practice Quiz

Explore Trees, Bagging & Random Forests through 20 original single-select and multiselect questions, from core understanding to worked applications, assumption checks and advanced diagnostic challenges.

Prerequisites: the preceding Statistics curriculum, including probability, conditional distributions, regression, model validation and regularization.

Practice goals: Explain tree predictions and bootstrap aggregation; Compute impurity gains, residual reductions and OOB probabilities; Assess pruning, ensemble variance and feature importance; Diagnose interactions, OOB selection and deployment leakage; Analyze tree invariances and extrapolation; Distinguish forest diversity from additional Monte Carlo averaging. Explanations work through the reasoning and address the alternatives.

Further reading: tree; ensemble.