code: def rfc_cv(
      n_estimators, min_samples_split, max_features, data, targets
  ):
      """Random Forest cross validation.
  
      This function will instantiate a random forest classifier with parameters
      n_estimators, min_samples_split, and max_features. Combined with data and
      targets this will in turn be used to perform cross validation. The result
      of cross validation is returned.
  
      Our goal is to find combinations of n_estimators, min_samples_split, and
      max_features that minimizes the log loss.
      """
      estimator = RFC(
          n_estimators=n_estimators,
          min_samples_split=min_samples_split,
          max_features=max_features,
          random_state=2,
      )
      cval = cross_val_score(
          estimator, data, targets, scoring="neg_log_loss", cv=4
      )
      return cval.mean()
  
file_path: BayesianOptimization/examples/sklearn_example.py
imports: from sklearn.datasets import make_classification
  from sklearn.model_selection import cross_val_score
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.svm import SVC
  from bayes_opt import BayesianOptimization
  from bayes_opt.util import Colours
label_desc: Use image node_id 3 for calling a global function with example usage: rfc_cv(n_estimators, min_samples_split, max_features, data, targets) and returns: cval