code: def scoring_on_bootstrap(estimator, X, y, rng, n_bootstrap=100):
      results_for_prevalence = defaultdict(list)
      for _ in range(n_bootstrap):
          bootstrap_indices = rng.choice(
              np.arange(X.shape[0]), size=X.shape[0], replace=True
          )
          for key, value in scoring(
              estimator, X[bootstrap_indices], y[bootstrap_indices]
          ).items():
              results_for_prevalence[key].append(value)
      return pd.DataFrame(results_for_prevalence)
  
file_path: scikit-learn/examples/model_selection/plot_likelihood_ratios.py
imports: from sklearn.datasets import make_classification
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import class_likelihood_ratios
  import pandas
  from sklearn.model_selection import cross_validate
  from sklearn.dummy import DummyClassifier
  from collections import defaultdict
  import matplotlib.pyplot
  import numpy
  from sklearn.inspection import DecisionBoundaryDisplay
label_desc: Use image node_id 3 for calling a global function with example usage: scoring_on_bootstrap(estimator, X, y, rng, n_bootstrap) and returns: pd