Fitting Bridge for the Parsnip Interface

fit_figs() is the function parsnip calls when fitting a figs_tree() specification with the "figsr" engine. It accepts either the formula interface or the x/y interface and forwards to figs(). Users normally call figs() or fit() instead.

Usage

fit_figs(
  formula = NULL,
  data = NULL,
  x = NULL,
  y = NULL,
  max_splits = 10,
  max_trees = NULL,
  min_n = 5,
  mode = "regression",
  na_method = c("omit", "mia"),
  ...
)

Arguments

formula

A formula specifying outcome and predictors.

data

A data frame containing the training data.

x

A data frame or matrix of predictors.

y

An outcome vector.

max_splits

Integer. Maximum total number of splits across all trees.

max_trees

Integer. Maximum number of trees in the sum.

min_n

Integer. Minimum number of observations in a node to split.

mode

Character. Either "regression" or "classification"; classification supports two-class outcomes only.

na_method

Character. "omit" or "mia"; see figs(). Set it as an engine argument: set_engine("figsr", na_method = "mia").

...

Additional arguments passed to figs().

Value

An object of class figsr_fit.

Examples

set.seed(42)
df <- data.frame(x1 = rnorm(60), x2 = rnorm(60))
df$y <- 2 * (df$x1 > 0) + rnorm(60, sd = 0.2)
fit_figs(x = df[, c("x1", "x2")], y = df$y, max_splits = 3)
#> ========================================================
#>   FIGS: Fast Interpretable Greedy-Tree Sums Model
#> ========================================================
#> Mode              : regression
#> Total Trees       : 1
#> Total Splits      : 3 / 3 (max_splits)
#> Predictors        : x1, x2
#> Used in Splits    : x1, x2
#> Missing values    : omit
#> ========================================================
#> 
#> Use `summary(fit)` to display detailed decision rules.
#> Use `plot(fit)` to visualize decision tree structures.