Extract rules from a BART model — extract

Extract interpretable decision rules from a single tree in a BART (Bayesian Additive Regression Trees) model. Each terminal node (leaf) becomes one rule representing the path from root to that leaf.

Usage

# S3 method for class 'bart'
extract_rules(x, tree = 1L, chain = 1L, ...)

Arguments

x: A bart object from the dbarts package fitted with keeptrees = TRUE.
tree: Integer specifying which tree to extract rules from. Uses 1-based indexing (default is 1L). BART models contain n.trees trees in the ensemble.
chain: Integer specifying which MCMC chain to extract from. Uses 1-based indexing (default is 1L). Only relevant for models fitted with multiple chains.
...: Not currently used.

Value

A tibble with class c("rule_set_bart", "rule_set") and columns:

tree: integer, the tree number (matches input parameter).
rules: list of R expressions, one per terminal node.
id: integer, terminal node ID (1-based).

Details

The BART model must be fitted with keeptrees = TRUE to enable tree extraction. This function uses 1-based indexing for the tree parameter and output id column (R convention).

Split conditions in BART follow the pattern: left child when feature < threshold, right child when feature >= threshold. Rules are combinations of these conditions using AND logic.

Examples

if (rlang::is_installed(c("dbarts", "palmerpenguins"))) {
  # Classification example
  data(penguins, package = "palmerpenguins")
  penguins <- na.omit(penguins)

  train_data <- penguins[, c("bill_length_mm", "bill_depth_mm",
                             "flipper_length_mm", "body_mass_g", "species")]

  set.seed(2847)
  fit <- dbarts::bart(
    x.train = train_data[, 1:4],
    y.train = train_data$species,
    keeptrees = TRUE,
    verbose = FALSE,
    ntree = 2
  )

  # Extract rules from first tree
  rules <- extract_rules(fit, tree = 1L)

  # View as text
  rule_text(rules$rules[[1]])

  # Regression example
  data(mtcars)
  set.seed(5193)
  fit_reg <- dbarts::bart(
    x.train = mtcars[, -1],
    y.train = mtcars$mpg,
    keeptrees = TRUE,
    verbose = FALSE,
    ntree = 2
  )
  rules_reg <- extract_rules(fit_reg, tree = 1L)
}