code: def get_toy_classification_data(
      target="target",
      n_samples=300,
      n_classes=2,
      shuffle=True,
      random_state=32,
      **kwargs,
  ):
      """Wrapper around `sklearn.datasets.make_classification` to produce a `pandas.DataFrame`"""
      x, y = make_classification(
          n_samples=n_samples,
          n_classes=n_classes,
          shuffle=shuffle,
          random_state=random_state,
          **kwargs,
      )
      train_df = pd.DataFrame(data=x, columns=range(x.shape[1]))
      train_df[target] = y
      return train_df
  
file_path: hyperparameter_hunter/hyperparameter_hunter/utils/learning_utils.py
imports: import pandas
  from sklearn.datasets import load_boston, load_breast_cancer, load_diabetes, load_iris, make_classification
label_desc: Use image node_id 6 for calling a global function with example usage: get_toy_classification_data(target, n_samples, n_classes, shuffle, random_state) and returns: train_df