text: import numpy as np
  
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  
  # NOTE: Make sure that the class is labeled 'class' in the data file
  tpot_data = np.recfromcsv('PATH/TO/DATA/FILE', delimiter='COLUMN_SEPARATOR', dtype=np.float64)
  features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1), tpot_data.dtype.names.index('class'), axis=1)
  training_features, testing_features, training_classes, testing_classes = \
      train_test_split(features, tpot_data['class'], random_state=42)
  
  exported_pipeline = RandomForestClassifier(bootstrap=False, max_features=0.4, min_samples_leaf=1, min_samples_split=9)
  
  exported_pipeline.fit(training_features, training_classes)
  results = exported_pipeline.predict(testing_features)
  