| English | 中文 |
A forest averages randomized trees to reduce instability relative to an individual tree. Per-split variable sampling, per-tree row sampling and tree complexity jointly affect diversity and local fit. More trees mainly improve numerical stability; they do not repair leakage, dependent samples or weak extrapolation. Assess prediction and interpretation stability under the intended evaluation design.
Parameter reasoning and cited evidence: model comparison.
R uses ranger and Python uses scikit-learn; use backend-specific parameter meanings, ranges and seed handling. Configuration, code calls and current checks belong to the matching skill.
Use the shared evaluation design and submit a model plan before generating experiment code.