text: import numpy as np
  import pandas as pd
  from sklearn.ensemble import ExtraTreesClassifier
  from sklearn.feature_selection import RFE, SelectPercentile, f_classif
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import make_pipeline, make_union
  from sklearn.preprocessing import MaxAbsScaler
  from sklearn.svm import LinearSVC
  from tpot.builtins import StackingEstimator
  from sklearn.preprocessing import FunctionTransformer
  from copy import copy
  
  # NOTE: Make sure that the outcome column 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)
  training_features, testing_features, training_target, testing_target = \
              train_test_split(features, tpot_data['target'], random_state=None)
  
  # Average CV score on the training set was: 0.8318355333451303
  exported_pipeline = make_pipeline(
      make_union(
          RFE(estimator=ExtraTreesClassifier(criterion="gini", max_features=0.45, n_estimators=100), step=0.05),
          FunctionTransformer(copy)
      ),
      StackingEstimator(estimator=ExtraTreesClassifier(bootstrap=False, criterion="entropy", max_features=0.8, min_samples_leaf=18, min_samples_split=11, n_estimators=100)),
      SelectPercentile(score_func=f_classif, percentile=67),
      MaxAbsScaler(),
      LinearSVC(C=0.5, dual=True, loss="squared_hinge", penalty="l2", tol=1e-05)
  )
  
  exported_pipeline.fit(training_features, training_target)
  results = exported_pipeline.predict(testing_features)
  