How to Develop a Bagging Ensemble with Python

Last Updated on September 7, 2020

Bagging is an ensemble machine learning algorithm that combines the predictions from many decision trees.

It is also easy to implement given that it has few key hyperparameters and sensible heuristics for configuring these hyperparameters.

Bagging performs well in general and provides the basis for a whole field of ensemble of decision tree algorithms such as the popular random forest and extra trees ensemble algorithms, as well as the lesser-known Pasting, Random Subspaces, and Random Patches ensemble algorithms.

In this tutorial, you will discover how to develop Bagging ensembles for classification and regression.

After completing this tutorial, you will know:

• Bagging ensemble is an ensemble created from decision trees fit on different samples of a dataset.
• How to use the Bagging ensemble for classification and regression with scikit-learn.
• How to explore the effect of Bagging model hyperparameters on model performance.

Let’s get started.

• Update Aug/2020: Added a common questions section.