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Decision trees and random forests share a partition-based predictor structure. A single tree gives one set of local rules; a forest averages randomized trees for greater stability. Choose between them using the scientific question, sample support, prediction stability and computational budget.
Keep variable groups, sample keys, preprocessing and evaluation population comparable. Shared ancestry does not make parameter meanings or numerical outputs interchangeable across R/Python implementations. Model comparison retains the cited parameter-selection evidence. Configuration and code belong to the tree-model skill directory; the mean reference belongs to common evaluation.