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
docstr: Sets decision function based on prediction_task
          
code: def _init_decision_function(self):
      """Sets decision function based on prediction_task"""
      # used by sklearn GridSearchCV, BaggingClassifier
      if self.prediction_task == "classification":
  
          def decision_function(x):
              return self.predict_proba(x)[:, 1]
  
      elif self.prediction_task == "regression":
          decision_function = self.predict
  
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 3 for calling the FIGSExt obj's underlying member method code with example usage: obj._init_decision_function() and returns: self