text: import numpy as np
  import pandas as pd
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  
  # NOTE: Make sure that the class is labeled 'target' in the data file
  tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)
  features = tpot_data.drop('target', axis=1).values
  training_features, testing_features, training_target, testing_target = \
              train_test_split(features, tpot_data['target'].values, random_state=None)
  
  # Average CV score on the training set was:0.564904143475572
  exported_pipeline = RandomForestClassifier(bootstrap=True, criterion="entropy", max_features=0.8, min_samples_leaf=10, min_samples_split=17, n_estimators=100)
  
  exported_pipeline.fit(training_features, training_target)
  results = exported_pipeline.predict(testing_features)
  