A bagging classifier.

bagging.py

# Bagging creates several models that rely on the same algorithm.
# The training of each model uses a different subset of data sampled randomly from the training set.
# By default Bagging uses soft voting when its base estimator can provide its measure of confidence,
# Hence the SVC model is set to have probability=True
from sklearn.svm import SVC
from sklearn.ensemble import BaggingClassifier

bagging_clf = BaggingClassifier(SVC(gamma='scale', probability=True, random_state=42),
                                bootstrap=True, # set to False to use Pasting instead of Bagging
                                n_estimators=100, # number of SVC models to create
                                max_samples=100, # each model is trained from randomly sampled 100 instances 
                                random_state=42
                               )
bagging_clf.fit(X_train, y_train) # training
y_pred_bagging = bagging_clf.predict(X_test) # predicting
accuracy_score(y_test, y_pred_bagging) # evaluating

# Output of the evaluation: 0.904