code: def rfc_cv(n_estimators, min_samples_split, max_features, data, targets):
      
      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()
docs: 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 minimzes the log loss.