PYTHON

bagging algorithm

# 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))