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figsr: Fast Interpretable Greedy-Tree Sums for R

figsr is an R implementation of Fast Interpretable Greedy-Tree Sums (‘FIGS’), developed by researchers at UC Berkeley and Stanford (Tan et al., PNAS 2023, https://doi.org/10.1073/pnas.2310151122).

Unlike standard single decision trees (‘CART’) which suffer from inductive bias against additive structures and repeat subtrees, figsr greedily grows a sum of shallow decision trees ( \(\hat{f}(x) = \sum_k \hat{f}_k(x)\) ). It achieves prediction accuracy close to random forests or gradient boosting while remaining human-interpretable with concise decision rules.

Installation

Install the released version from CRAN:

install.packages("figsr")

Or the development version from GitHub:

# install.packages("remotes")
remotes::install_github("bonijoao/figsr")

Quick Example with tidymodels

figsr seamlessly integrates with parsnip and tidymodels using native pipe syntax (|> or %>%):

library(tidymodels)
library(figsr)

# 1. Simulate additive data
set.seed(42)
df <- tibble(
  x1 = rnorm(300),
  x2 = rnorm(300),
  x3 = rnorm(300),
  y  = 3 * (x1 > 0) + 2 * (x2 > 0.5) - 1.5 * (x3 < -0.2) + rnorm(300, sd = 0.3)
)

# 2. Specify FIGS model using parsnip
figs_spec <- figs_tree(max_splits = 6, min_n = 5) |>
  set_engine("figsr") |>
  set_mode("regression")

# 3. Fit workflow
figs_fit <- df |>
  recipe(y ~ x1 + x2 + x3) |>
  workflow(figs_spec) |>
  fit(data = df)

# 4. Predict tidy tibble
preds <- predict(figs_fit, new_data = df)
head(preds)

Features

• Tidymodels & Parsnip Integration: figs_tree() model specification.
• Hyperparameter Tuning: Tune max_splits and max_trees with tune_grid().
• Interpretability & Visualization: summary(fit) prints logical IF-THEN rules; plot(fit) draws visual tree sums.
• Variable Importance: figsr_importance(fit) ranks feature impurity reductions.
• Bagging-FIGS: Bootstrap ensembling via bagging_figs().

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.