How to Develop a Bagging Ensemble with Python

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. How to Develop a Bagging Ensemble in Python Photo by daveynin, some rights reserved. Bootstrap Aggregation, or Bagging for short, is an ensemble machine learning algorithm. Specifically, it is an ensemble of decision tree models, although the bagging technique can also be used to combine the predictions of other types of models.