class_docstr: FIGSExt (sum of trees) classifier.
  Fast Interpretable Greedy-Tree Sums (FIGS) is an algorithm for fitting concise rule-based models.
  Specifically, FIGS generalizes CART to simultaneously grow a flexible number of trees in a summation.
  The total number of splits across all the trees can be restricted by a pre-specified threshold, keeping the model interpretable.
  Experiments across a wide array of real-world datasets show that FIGS achieves state-of-the-art prediction performance when restricted to just a few splits (e.g. less than 20).
  https://arxiv.org/abs/2201.11931
code: def _tree_to_str(self, root: Node, prefix=""):
      if root is None:
          return ""
      elif root.split_or_linear == "linear":
          return prefix + str(root)
      elif root.threshold is None:
          return ""
      pprefix = prefix + "\t"
      return (
          prefix
          + str(root)
          + "\n"
          + self._tree_to_str(root.left, pprefix)
          + self._tree_to_str(root.right, pprefix)
      )
  
imports: from copy import deepcopy
  import numpy
  from matplotlib import pyplot
  from sklearn import datasets
  from sklearn import tree
  from sklearn.base import BaseEstimator
  from sklearn.linear_model import RidgeCV, RidgeClassifierCV
  from sklearn.model_selection import train_test_split
  from sklearn.tree import plot_tree
  from sklearn.utils import check_X_y
  from imodels.tree.viz_utils import extract_sklearn_tree_from_figs
label_desc: Use image node_id 7 for calling the FIGSExt obj's underlying member method code with example usage: obj._tree_to_str(root, prefix) and returns: unknown, str, unknown, str