What are bagging ensemble methods in Python machine learning?

What are bagging ensemble methods in Python machine learning?

Bagging, or Bootstrap Aggregating, is an ensemble method that improves the stability and accuracy of machine learning algorithms by combining multiple models. It works by training multiple models on different subsets of the training data and then averaging their predictions.

How does bagging differ from boosting?

While both bagging and boosting are ensemble methods, bagging trains models independently and combines their predictions, whereas boosting trains models sequentially, with each model focusing on the errors of the previous one.

Can you give me an example of bagging in Python?

Sure! You can use the BaggingClassifier from the sklearn.ensemble module. Here's a simple example:

from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier

# Create a base classifier
base_classifier = DecisionTreeClassifier()
# Create a bagging classifier
bagging_classifier = BaggingClassifier(base_estimator=base_classifier, n_estimators=10)