# Import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
# Import BaggingClassifier
from sklearn.ensemble import BaggingClassifier
# Instantiate dt
dt = DecisionTreeClassifier(random_state=1)
# Instantiate bc
bc = BaggingClassifier(base_estimator=dt,
n_estimators=...number of classification trees...,
random_state=1,
oob_score=True,
n_jobs=-1)
# Fit bc to the training set
bc.fit(X_train, y_train)
# Predict test set labels
y_pred = bc.predict(X_test)
# Evaluate OOB accuracy
acc_oob = bc.oob_score_
# Evaluate acc_test
acc_test = accuracy_score(y_test, y_pred)
# Print acc_test and acc_oob
print('Test set accuracy: {:.3f}, OOB accuracy: {:.3f}'.format(acc_test, acc_oob))