Grow Extreme Trees — grow_extreme

XGBoost Boosted Forest: each boosting round grows a forest of num_parallel_tree trees rather than a single tree. Based on the XGBoost random forest + boosting hybrid described at https://xgboost.readthedocs.io/en/stable/tutorials/rf.html.

Usage

grow_extreme_trees(
  x,
  y,
  trees = 50L,
  task = "classification",
  num_parallel_tree = 10L,
  exotic = FALSE,
  ...
)

Arguments

x: a data X
y: a data Y
trees: num of boosting rounds (each round grows a forest)
task: a task
num_parallel_tree: number of trees per forest per round
exotic: logical; if TRUE, apply additional exotic parameters
...: other eng args

Value

a fitted model

Details

When exotic = FALSE (default), parameters follow the paper-faithful boosted random forest configuration: gradient-based row sampling with moderate column subsampling per node.

When exotic = TRUE, additional daisugi params are applied: cumulative column sampling across all three colsample_by* levels, reduced lambda for deeper trees, and leaf-wise (lossguide) growth within each forest member.