code: def create_binary_classification_dataset(n_samples=100):
      """Create a binary classification dataset.
  
      param n_samples: Number of samples.
      type n_samples: int
      return: Tuple of X_train, y_train, X_test, y_test, classes.
      rtype: Tuple of pandas.DataFrame, pandas.DataFrame, numpy.ndarray,
             numpy.ndarray, list
      """
      X, y = make_classification(n_samples=n_samples, random_state=777)
  
      # Split data into train and test
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.2, random_state=0
      )
      classes = np.unique(y_train).tolist()
      feature_names = [
          "col" + str(i) for i in list(range(X_train.shape[1]))
      ]
      X_train = pd.DataFrame(X_train, columns=feature_names)
      X_test = pd.DataFrame(X_test, columns=feature_names)
      return X_train, y_train, X_test, y_test, classes
  
file_path: responsible-ai-toolbox/rai_test_utils/rai_test_utils/datasets/tabular/classification_data_utils.py
imports: import numpy
  import pandas
  import shap
  from pandas import read_csv
  from sklearn.datasets import load_breast_cancer, load_iris, load_wine, make_classification
  from sklearn.model_selection import train_test_split
  from rai_test_utils.utilities import retrieve_dataset
label_desc: Use image node_id 5 for calling a global function with example usage: create_binary_classification_dataset(n_samples) and returns: X_train,  y_train,  X_test,  y_test,  classes