The website is in Maintenance mode. We are in the process of adding more features.
Any new bookmarks, comments, or user profiles made during this time will not be saved.

AIML.com

Machine Learning Resources

What is the difference between Decision Trees, Bagging and Random Forest?

Bookmark this question

A decision tree serves as the building block of most bagging and boosting algorithms and is always built using the concept of maximizing information. Bagging, and specifically Random Forest, provides a mechanism for constructing an ensemble of decision trees, which creates a prediction that results from the aggregation of all trees in the ensemble. Random Forest is a specific example of a bagging method that creates each decision tree using a bootstrap sample of the original data and then performs aggregation to determine a final prediction for each observation. 

Leave your Comments and Suggestions below:

Please Login or Sign Up to leave a comment

Partner Ad  

Find out all the ways
that you can

Explore Questions by Topics

Partner Ad

Learn Data Science with Travis - your AI-powered tutor | LearnEngine.com