ensemble_imbalance.py

from imblearn.ensemble import BalancedBaggingClassifier
from sklearn.tree import DecisionTreeClassifier

#Create an object of the classifier.
bbc = BalancedBaggingClassifier(base_estimator=DecisionTreeClassifier(),
                                sampling_strategy='auto',
                                replacement=False,
                                random_state=0)

y_train = credit_df['Class']
X_train = credit_df.drop(['Class'], axis=1, inplace=False)

#Train the classifier.
bbc.fit(X_train, y_train)
preds = bbc.predict(X_test)