text: import numpy as np
  import pandas as pd
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import PolynomialFeatures, StandardScaler
  from sklearn.svm import LinearSVC
  from sklearn.tree import DecisionTreeClassifier
  from tpot.builtins import StackingEstimator
  from tpot.export_utils import set_param_recursive
  
  # 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=42
  )
  
  # Average CV score on the training set was: 0.8636913680548279
  exported_pipeline = make_pipeline(
      PolynomialFeatures(degree=2, include_bias=False, interaction_only=False),
      StandardScaler(),
      StackingEstimator(
          estimator=LinearSVC(
              C=15.0, dual=False, loss="squared_hinge", penalty="l2", tol=0.0001
          )
      ),
      StackingEstimator(
          estimator=LinearSVC(
              C=15.0, dual=False, loss="squared_hinge", penalty="l1", tol=0.0001
          )
      ),
      StackingEstimator(
          estimator=GradientBoostingClassifier(
              learning_rate=0.1,
              max_depth=6,
              max_features=0.15000000000000002,
              min_samples_leaf=19,
              min_samples_split=4,
              n_estimators=100,
              subsample=0.5,
          )
      ),
      DecisionTreeClassifier(
          criterion="gini", max_depth=9, min_samples_leaf=2, min_samples_split=8
      ),
  )
  # Fix random state for all the steps in exported pipeline
  set_param_recursive(exported_pipeline.steps, "random_state", 42)
  
  exported_pipeline.fit(training_features, training_target)
  results = exported_pipeline.predict(testing_features)
  