docstr: Generate a classification/regression dataset.
docstr_tok: ["Generate","a","classification\/regression","dataset","."]
code: def generate_dataset(self, is_classification=False, is_skewed=False):
      """Generate a classification/regression dataset."""
      X1 = np.arange(1000)
      if is_classification:
          y = np.round(np.sin(X1)).astype("int")
      else:
          y = np.sin(X1)
  
      if is_classification and is_skewed:
          y[0] = 2
  
      X2 = np.cos(X1)
      X3 = np.cos(X1)
  
      train_numpy = np.hstack(
          (y.reshape(-1, 1), X2.reshape(-1, 1), X3.reshape(-1, 1))
      )
      train = pd.DataFrame(
          data=train_numpy, columns=["target_column", "col2", "col3"]
      )
      target_column_name = "target_column"
  
      if is_classification:
          classes = np.unique(y).tolist()
      else:
          classes = None
  
      return train, target_column_name, classes
  
file_path: responsible-ai-toolbox/raiutils/tests/test_sampling.py
imports: import numpy
  import pandas
  from raiutils.sampling import generate_random_sample
label_desc: Use image node_id 2 for calling the TestRandomSampling obj's underlying member method code with example usage: obj.generate_dataset(is_classification, is_skewed) and returns: train,  target_column_name,  classes