build.tree: Recursive Approach for Building Decision Tree
In ebridge2/badmf: Bayesian Decision-Making Forests (BaD-MFs)

Recursive Approach for Building Decision Tree

1	build.tree(split, d, alpha, depth.max, size, depth, debug = FALSE)

`split`	a particular split node.
`d`	the number of features to subsample.
`alpha`	the sampling distribution for the features. A `[p]` vector. If `NULL`, uses a uniform.
`depth.max`	the max tree depth.
`size`	the minimum number of elements at a particular.
`depth`	the current depth of the tree.
`debug`	a boolean indicating whether to save the predictors and responses that are categorized into leaf nodes.