Bagging - Webdesk AI Glossary

Bagging, short for Bootstrap Aggregating, is an ensemble learning technique used in machine learning to improve the stability and accuracy of algorithms, particularly decision trees. Here's a breakdown of how bagging works:

Advantages of bagging include:

An example of a bagging algorithm is the Random Forest, which combines the simplicity of decision trees with the power of ensemble learning, resulting in robust and accurate models.

Webdesk AI Glossary : Bagging