code: def print_cv_info(cv, X, y, groups, clf, scores):
      """Print information details about cross-validation usage with classifier."""
      for split_idx, (train_idx, test_idx) in enumerate(
          cv.split(X, y, groups)
      ):
          clf.fit(X.iloc[train_idx], y.iloc[train_idx])
          y_train_pred = clf.predict(X.iloc[train_idx])
          y_test_pred = clf.predict(X.iloc[test_idx])
          print(f"Split number: {split_idx + 1}")
          print(f"Train true target: {y.iloc[train_idx].values}")
          print(f"Train predicted target: {y_train_pred}")
          print(f"Test true target: {y.iloc[test_idx].values}")
          print(f"Test predicted target: {y_test_pred}")
          print(f"Accuracy: {scores[split_idx].round(2)}")
          print()
  
file_path: mlxtend/mlxtend/evaluate/time_series.py
imports: from itertools import accumulate, chain, groupby, islice
  import matplotlib.pyplot
  import numpy
  from matplotlib.patches import Patch
  from matplotlib.ticker import MaxNLocator
  from sklearn.utils import indexable
label_desc: Use image node_id 4 for calling a global function with example usage: print_cv_info(cv, X, y, groups, clf, scores) without return types